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Semantic disambiguation or the understanding of natural language are areas within the natural language processing that although they have been widely studied, continue to pose a major challenge. Traditional approaches to semantic disambiguation lie on the assumption that there is a unique and unequivocal semantic underlying each word in a sentence. However, there is a class of constructions in language known as puns, in which lexical-semantic ambiguity is a sought-after effect in the sentence. That is, the speaker or writer pretends that a particular word or other lexical element is interpreted simultaneously with two or more different meanings. In this project we propose to approach the location and disambiguation of double meaning words (puns) in a set of sentences. To do this we will use different vector representations of words obtained from different corpus, and different metrics of similarity will be studied. The data sets belong to the Semeval 2017 international competition and the results can be compared with those published by the competition., La desambiguación semántica o la comprensión del lenguaje natural son ámbitos dentro del procesamiento del lenguaje natural que aunque han sido ampliamente estudiados, siguen suponiendo un reto importante. Los enfoques tradicionales de la desambiguación semántica descansan en la suposición de que existe una única e inequívoca semántica subyacente a cada palabra en una oración. Sin embargo, existe una clase de construcciones en el lenguaje conocidas como juegos de palabras (puns), en los que la ambigüedad léxico-semántica es un efecto buscado en la oración. Es decir, el hablante o escritor pretende que una determinada palabra u otro elemento léxico sea interpretado simultáneamente con dos o más significados distintos. En este proyecto proponemos abordar la localización y desambiguación de palabras con doble sentido (puns) en una serie de oraciones. Para ello usaremos diferentes representaciones vectoriales de las palabras obtenidas a partir de diferentes corpus, y se estudiarán diferentes métricas de similitud. Los conjuntos de datos pertenecen a la competición internacional Semeval 2017 y los resultados se podrán contrastar con los publicados por la competición., [CA] La desambiguació semàntica o la comprensió del llenguatge natural són àmbits dins del processament del llenguatge natural que encara que han estat àmpliament estudiats, segueixen suposant un repte important. Els enfocaments tradicionals de la desambiguació semàntica descansen en la suposició que hi ha una única e inequívoca semàntica subjacent a cada paraula en una oració. No obstant això, hi ha una classe de construccions en el llenguatge conegudes com jocs de paraules (puns), en els quals l’ambigüitat lexicosemàntica és un efecte buscat en l’oració. És a dir, el parlant o escriptor pretén que una determinada paraula o un altre element lèxic sigui interpretat simultàniament amb dos o més significats diferents. En aquest projecte proposem abordar la localització i desambiguació de paraules amb doble sentit (puns) en una sèrie d’oracions. Per a això farem servir diferents representacions vectorials de les paraules obtingudes a partir de diferents corpus, i s’estudiaran diferents mètriques de similitud. Els conjunts de dades pertanyen a la competició internacional Semeval 2017 i els resultats es podran contrastar amb les publicades per la competició.

Semantic disambiguation or the understanding of natural language are areas within the natural language processing that although they have been widely studied, continue to pose a major challenge. Traditional approaches to semantic disambiguation lie on the assumption that there is a unique and unequivocal semantic underlying each word in a sentence. However, there is a class of constructions in language known as puns, in which lexical-semantic ambiguity is a sought-after effect in the sentence. That is, the speaker or writer pretends that a particular word or other lexical element is interpreted simultaneously with two or more different meanings. In this project we propose to approach the location and disambiguation of double meaning words (puns) in a set of sentences. To do this we will use different vector representations of words obtained from different corpus, and different metrics of similarity will be studied. The data sets belong to the Semeval 2017 international competition and the results can be compared with those published by the competition., La desambiguación semántica o la comprensión del lenguaje natural son ámbitos dentro del procesamiento del lenguaje natural que aunque han sido ampliamente estudiados, siguen suponiendo un reto importante. Los enfoques tradicionales de la desambiguación semántica descansan en la suposición de que existe una única e inequívoca semántica subyacente a cada palabra en una oración. Sin embargo, existe una clase de construcciones en el lenguaje conocidas como juegos de palabras (puns), en los que la ambigüedad léxico-semántica es un efecto buscado en la oración. Es decir, el hablante o escritor pretende que una determinada palabra u otro elemento léxico sea interpretado simultáneamente con dos o más significados distintos. En este proyecto proponemos abordar la localización y desambiguación de palabras con doble sentido (puns) en una serie de oraciones. Para ello usaremos diferentes representaciones vectoriales de las palabras obtenidas a partir de diferentes corpus, y se estudiarán diferentes métricas de similitud. Los conjuntos de datos pertenecen a la competición internacional Semeval 2017 y los resultados se podrán contrastar con los publicados por la competición., [CA] La desambiguació semàntica o la comprensió del llenguatge natural són àmbits dins del processament del llenguatge natural que encara que han estat àmpliament estudiats, segueixen suposant un repte important. Els enfocaments tradicionals de la desambiguació semàntica descansen en la suposició que hi ha una única e inequívoca semàntica subjacent a cada paraula en una oració. No obstant això, hi ha una classe de construccions en el llenguatge conegudes com jocs de paraules (puns), en els quals l’ambigüitat lexicosemàntica és un efecte buscat en l’oració. És a dir, el parlant o escriptor pretén que una determinada paraula o un altre element lèxic sigui interpretat simultàniament amb dos o més significats diferents. En aquest projecte proposem abordar la localització i desambiguació de paraules amb doble sentit (puns) en una sèrie d’oracions. Per a això farem servir diferents representacions vectorials de les paraules obtingudes a partir de diferents corpus, i s’estudiaran diferents mètriques de similitud. Els conjunts de dades pertanyen a la competició internacional Semeval 2017 i els resultats es podran contrastar amb les publicades per la competició.

Semantic disambiguation or the understanding of natural language are areas within the natural language processing that although they have been widely studied, continue to pose a major challenge. Traditional approaches to semantic disambiguation lie on the assumption that there is a unique and unequivocal semantic underlying each word in a sentence. However, there is a class of constructions in language known as puns, in which lexical-semantic ambiguity is a sought-after effect in the sentence. That is, the speaker or writer pretends that a particular word or other lexical element is interpreted simultaneously with two or more different meanings. In this project we propose to approach the location and disambiguation of double meaning words (puns) in a set of sentences. To do this we will use different vector representations of words obtained from different corpus, and different metrics of similarity will be studied. The data sets belong to the Semeval 2017 international competition and the results can be compared with those published by the competition., La desambiguación semántica o la comprensión del lenguaje natural son ámbitos dentro del procesamiento del lenguaje natural que aunque han sido ampliamente estudiados, siguen suponiendo un reto importante. Los enfoques tradicionales de la desambiguación semántica descansan en la suposición de que existe una única e inequívoca semántica subyacente a cada palabra en una oración. Sin embargo, existe una clase de construcciones en el lenguaje conocidas como juegos de palabras (puns), en los que la ambigüedad léxico-semántica es un efecto buscado en la oración. Es decir, el hablante o escritor pretende que una determinada palabra u otro elemento léxico sea interpretado simultáneamente con dos o más significados distintos. En este proyecto proponemos abordar la localización y desambiguación de palabras con doble sentido (puns) en una serie de oraciones. Para ello usaremos diferentes representaciones vectoriales de las palabras obtenidas a partir de diferentes corpus, y se estudiarán diferentes métricas de similitud. Los conjuntos de datos pertenecen a la competición internacional Semeval 2017 y los resultados se podrán contrastar con los publicados por la competición., [CA] La desambiguació semàntica o la comprensió del llenguatge natural són àmbits dins del processament del llenguatge natural que encara que han estat àmpliament estudiats, segueixen suposant un repte important. Els enfocaments tradicionals de la desambiguació semàntica descansen en la suposició que hi ha una única e inequívoca semàntica subjacent a cada paraula en una oració. No obstant això, hi ha una classe de construccions en el llenguatge conegudes com jocs de paraules (puns), en els quals l’ambigüitat lexicosemàntica és un efecte buscat en l’oració. És a dir, el parlant o escriptor pretén que una determinada paraula o un altre element lèxic sigui interpretat simultàniament amb dos o més significats diferents. En aquest projecte proposem abordar la localització i desambiguació de paraules amb doble sentit (puns) en una sèrie d’oracions. Per a això farem servir diferents representacions vectorials de les paraules obtingudes a partir de diferents corpus, i s’estudiaran diferents mètriques de similitud. Els conjunts de dades pertanyen a la competició internacional Semeval 2017 i els resultats es podran contrastar amb les publicades per la competició.

Words can take on many meanings, and collecting and identifying example usages representative of the full variety of meanings words can take is a bottleneck to the study of lexical semantics using statistical approaches. To perform supervised word sense disambiguation (WSD), or to evaluate knowledge-based methods, a corpus of texts annotated with senses from a dictionary may be constructed by paid experts. However, the cost usually prohibits more than a small sample of words and senses being represented in the corpus. Crowdsourcing methods promise to acquire data more cheaply, albeit with a greater challenge for quality control. Most crowdsourcing to date has incentivised participation in the form of a payment or by gamification of the resource construction task. However, with paid crowdsourcing the cost of human labour scales linearly with the output size, and while game playing volunteers may be free, gamification studies must compete with a multi-billion dollar games industry for players. In this thesis we develop and evaluate resources for computational semantics, working towards a crowdsourcing method that extracts information from naturally occurring human activities. A number of software products exist for glossing Japanese text with entries from a dictionary for English speaking students. However, the most popular ones have a tendency to either present an overwhelming amount of information containing every sense of every word or else hide too much information and risk removing senses with particular relevance to a specific text. By offering a glossing application with interactive features for exploring word senses, we create an opportunity to crowdsource human judgements about word senses and record human interaction with semantic NLP.

Today, Information Retrieval (IR) provides users with documents that will satisfy their information need. Word sense ambiguity is a cause of poor performance in IR system. IR performance will be increased if ambiguous words can be correctly disambiguated. Word Sense disambiguation (WSD) is the task to assign the correct meaning to such ambiguous words based on the surrounding context. Various senses provided by WSD process have been used as semantics for indexing the documents to aid the information retrieval system. K-Nearest Neighbour (KNN) is used for effective text classification in WSD process and the Vector Space Model (VSM) is used for IR process. The cosine similarity method is used in both KNN and VSM to calculate the similarity in which term frequency and inverse document frequency (TF-IDF) scheme is used to calculate the weight of each word. There is a challenge that the original TF-IDF scheme eliminates the related senses although there is a related sense. This paper thus proposes the modified TF-IDF method, so called TF-MIDF, to solve the no-relevant problem by modifying the IDF equation to improve the accuracy of IR performance. By comparing the performance between the original IDF scheme and the MIDF scheme, the average precision results of the original TF-IDF method is 71% and the average precision results of the TF-MIDF is 80%. Therefore, the proposed methodology is more precise than the original method while retrieving the relevant documents of the required information.

Today, Information Retrieval (IR) provides users with documents that will satisfy their information need. Word sense ambiguity is a cause of poor performance in IR system. IR performance will be increased if ambiguous words can be correctly disambiguated. Word Sense disambiguation (WSD) is the task to assign the correct meaning to such ambiguous words based on the surrounding context. Various senses provided by WSD process have been used as semantics for indexing the documents to aid the information retrieval system. K-Nearest Neighbour (KNN) is used for effective text classification in WSD process and the Vector Space Model (VSM) is used for IR process. The cosine similarity method is used in both KNN and VSM to calculate the similarity in which term frequency and inverse document frequency (TF-IDF) scheme is used to calculate the weight of each word. There is a challenge that the original TF-IDF scheme eliminates the related senses although there is a related sense. This paper thus proposes the modified TF-IDF method, so called TF-MIDF, to solve the no-relevant problem by modifying the IDF equation to improve the accuracy of IR performance. By comparing the performance between the original IDF scheme and the MIDF scheme, the average precision results of the original TF-IDF method is 71% and the average precision results of the TF-MIDF is 80%. Therefore, the proposed methodology is more precise than the original method while retrieving the relevant documents of the required information.

Today, Information Retrieval (IR) provides users with documents that will satisfy their information need. Word sense ambiguity is a cause of poor performance in IR system. IR performance will be increased if ambiguous words can be correctly disambiguated. Word Sense disambiguation (WSD) is the task to assign the correct meaning to such ambiguous words based on the surrounding context. Various senses provided by WSD process have been used as semantics for indexing the documents to aid the information retrieval system. K-Nearest Neighbour (KNN) is used for effective text classification in WSD process and the Vector Space Model (VSM) is used for IR process. The cosine similarity method is used in both KNN and VSM to calculate the similarity in which term frequency and inverse document frequency (TF-IDF) scheme is used to calculate the weight of each word. There is a challenge that the original TF-IDF scheme eliminates the related senses although there is a related sense. This paper thus proposes the modified TF-IDF method, so called TF-MIDF, to solve the no-relevant problem by modifying the IDF equation to improve the accuracy of IR performance. By comparing the performance between the original IDF scheme and the MIDF scheme, the average precision results of the original TF-IDF method is 71% and the average precision results of the TF-MIDF is 80%. Therefore, the proposed methodology is more precise than the original method while retrieving the relevant documents of the required information.

Today, Information Retrieval (IR) provides users with documents that will satisfy their information need. Word sense ambiguity is a cause of poor performance in IR system. IR performance will be increased if ambiguous words can be correctly disambiguated. Word Sense disambiguation (WSD) is the task to assign the correct meaning to such ambiguous words based on the surrounding context. Various senses provided by WSD process have been used as semantics for indexing the documents to aid the information retrieval system. K-Nearest Neighbour (KNN) is used for effective text classification in WSD process and the Vector Space Model (VSM) is used for IR process. The cosine similarity method is used in both KNN and VSM to calculate the similarity in which term frequency and inverse document frequency (TF-IDF) scheme is used to calculate the weight of each word. There is a challenge that the original TF-IDF scheme eliminates the related senses although there is a related sense. This paper thus proposes the modified TF-IDF method, so called TF-MIDF, to solve the no-relevant problem by modifying the IDF equation to improve the accuracy of IR performance. By comparing the performance between the original IDF scheme and the MIDF scheme, the average precision results of the original TF-IDF method is 71% and the average precision results of the TF-MIDF is 80%. Therefore, the proposed methodology is more precise than the original method while retrieving the relevant documents of the required information.

Today, Information Retrieval (IR) provides users with documents that will satisfy their information need. Word sense ambiguity is a cause of poor performance in IR system. IR performance will be increased if ambiguous words can be correctly disambiguated. Word Sense disambiguation (WSD) is the task to assign the correct meaning to such ambiguous words based on the surrounding context. Various senses provided by WSD process have been used as semantics for indexing the documents to aid the information retrieval system. K-Nearest Neighbour (KNN) is used for effective text classification in WSD process and the Vector Space Model (VSM) is used for IR process. The cosine similarity method is used in both KNN and VSM to calculate the similarity in which term frequency and inverse document frequency (TF-IDF) scheme is used to calculate the weight of each word. There is a challenge that the original TF-IDF scheme eliminates the related senses although there is a related sense. This paper thus proposes the modified TF-IDF method, so called TF-MIDF, to solve the no-relevant problem by modifying the IDF equation to improve the accuracy of IR performance. By comparing the performance between the original IDF scheme and the MIDF scheme, the average precision results of the original TF-IDF method is 71% and the average precision results of the TF-MIDF is 80%. Therefore, the proposed methodology is more precise than the original method while retrieving the relevant documents of the required information.

In this paper we present an approach to Word Sense Disambiguation based on Topic Modeling (LDA). Our approach consists of two different steps, where first a binary classifier is applied to decide whether the most frequent sense applies or not, and then another classifier deals with the non most frequent sense cases. An exhaustive evaluation is performed on the Spanish corpus Ancora, to analyze the performance of our two-step system and the impact of the context and the different parameters in the system. Our best experiment reaches an accuracy of 74.53, which is 6 points over the highest baseline. All the software developed for these experiments has been made freely available, to enable reproducibility and allow the re-usage of the software., En este artículo se presenta una aproximación a la Desambiguación del Sentido de las Palabras basada en Modelado de Categorías (LDA). Nuestra aproximación consiste en dos pasos diferenciados, donde primero un clasificador binario se ejecuta para decidir si la heurística del sentido más frecuente se debe aplicar, y posteriormente otro clasificador se encarga del resto de sentidos donde esta heurística no corresponde. Se ha realizado una evaluación exhaustiva en el corpus en español Ancora, para analizar el funcionamiento de nuestro sistema de dos pasos y el impacto del contexto y de diferentes parámetros en dicho sistema. Nuestro mejor experimento alcanza un acierto de 74.53, lo cual es 6 puntos superior al baseline más alto. Todo el software desarrollado para estos experimentos se ha puesto disponible libremente para permitir la reprodubilidad de los experimentos y la reutilización del software.

Lexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed.

In this paper we present an approach to Word Sense Disambiguation based on Topic Modeling (LDA). Our approach consists of two different steps, where first a binary classifier is applied to decide whether the most frequent sense applies or not, and then another classifier deals with the non most frequent sense cases. An exhaustive evaluation is performed on the Spanish corpus Ancora, to analyze the performance of our two-step system and the impact of the context and the different parameters in the system. Our best experiment reaches an accuracy of 74.53, which is 6 points over the highest baseline. All the software developed for these experiments has been made freely available, to enable reproducibility and allow the re-usage of the software., En este artículo se presenta una aproximación a la Desambiguación del Sentido de las Palabras basada en Modelado de Categorías (LDA). Nuestra aproximación consiste en dos pasos diferenciados, donde primero un clasificador binario se ejecuta para decidir si la heurística del sentido más frecuente se debe aplicar, y posteriormente otro clasificador se encarga del resto de sentidos donde esta heurística no corresponde. Se ha realizado una evaluación exhaustiva en el corpus en español Ancora, para analizar el funcionamiento de nuestro sistema de dos pasos y el impacto del contexto y de diferentes parámetros en dicho sistema. Nuestro mejor experimento alcanza un acierto de 74.53, lo cual es 6 puntos superior al baseline más alto. Todo el software desarrollado para estos experimentos se ha puesto disponible libremente para permitir la reprodubilidad de los experimentos y la reutilización del software.

In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine translation, a word's context is determined by a phrase or sentences where the word occurs. In this project, the set of keywords represents the context. By investigating traditional approaches to machine translation (MT), we designed and described models for the specific purpose of keyword- translation. We have proposed a solution, based on direct translation for translating keywords between English and Swedish. In the proposed solu- tion, we also introduced a simple graph-based model for solving ambigu- ous translations., I detta projekt har vi undersökt hur man utför regelbaserad maskinöver- sättning av nyckelord mellan två språk. Målet var att översätta en given mängd med ett eller flera nyckelord på ett källspråk till en motsvarande, lika stor mängd nyckelord på målspråket. Vissa ord i källspråket kan dock ha flera betydelser och kan översättas till flera, eller inga, ord på målsprå- ket. Om tvetydiga översättningar uppstår ska nyckelordets bästa över- sättning väljas med hänsyn till sammanhanget. I traditionell maskinö- versättning bestäms ett ords sammanhang av frasen eller meningen som det befinner sig i. I det här projektet representerar den givna mängden nyckelord sammanhanget. Genom att undersöka traditionella tillvägagångssätt för maskinöversätt- ning har vi designat och beskrivit modeller specifikt för översättning av nyckelord. Vi har presenterat en direkt maskinöversättningslösning av nyckelord mellan engelska och svenska där vi introducerat en enkel graf- baserad modell för tvetydiga översättningar.

In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine translation, a word's context is determined by a phrase or sentences where the word occurs. In this project, the set of keywords represents the context. By investigating traditional approaches to machine translation (MT), we designed and described models for the specific purpose of keyword- translation. We have proposed a solution, based on direct translation for translating keywords between English and Swedish. In the proposed solu- tion, we also introduced a simple graph-based model for solving ambigu- ous translations., I detta projekt har vi undersökt hur man utför regelbaserad maskinöver- sättning av nyckelord mellan två språk. Målet var att översätta en given mängd med ett eller flera nyckelord på ett källspråk till en motsvarande, lika stor mängd nyckelord på målspråket. Vissa ord i källspråket kan dock ha flera betydelser och kan översättas till flera, eller inga, ord på målsprå- ket. Om tvetydiga översättningar uppstår ska nyckelordets bästa över- sättning väljas med hänsyn till sammanhanget. I traditionell maskinö- versättning bestäms ett ords sammanhang av frasen eller meningen som det befinner sig i. I det här projektet representerar den givna mängden nyckelord sammanhanget. Genom att undersöka traditionella tillvägagångssätt för maskinöversätt- ning har vi designat och beskrivit modeller specifikt för översättning av nyckelord. Vi har presenterat en direkt maskinöversättningslösning av nyckelord mellan engelska och svenska där vi introducerat en enkel graf- baserad modell för tvetydiga översättningar.

The aim of the article is to present several problems encountered by the editor compiling the Corpus of Wrocław University of Technology. The problems are connected with the application of the method of disambiguation of meanings of words in text (WSD – Word Sense Disambiguation) during the work on material of phraseological character. Any researcher using this method confronts the problem of the lack of semantic independence of words entering into the composition of fixed word combinations. The necessity to develop a unique procedure of conduct for this type of words, inclined the author of the article to experiment in the isolation of independent meanings of words co-forming phraseologisms and to make an attempt at their typologization. The isolation of such meanings has proved possible, although the accuracy of their description is very unstable: in some cases the motivation of semantic changes—which the meaning of a word undergoes under the influence of “being petrified” in a set phrase—is transparent, in other cases, however, we can speak of a hypothetical source of changes rather than a definite modification of meaning. It is worth mentioning here about the rigour imposed by the adopted WSD marking method: a set of all meanings which a given word form may assume in the analysed text must be entered into the database. That is why the problem of providing the definition of words forming phraseologisms is vital from the point of view of a linguist dealing with machine processing of language.

Geotechnical design has traditionally been done using working stress design (WSD). Working stress design identifies limit states for a structure, then protects against their occurrence using factors of safety. Load and resistance factor design (LRFD) uses load and resistance factors instead of factors of safety. LRFD also establishes the geotechnical resistance much more precisely than WSD. This research establishes load and resistance factors using reliability analysis and specifically lays out methods of calculation of resistance for four limit states related to MSE wall design: overturning, sliding, reinforcement pullout and reinforcement rupture.

Geotechnical design has traditionally been done using working stress design (WSD). Working stress design identifies limit states for a structure, then protects against their occurrence using factors of safety. Load and resistance factor design (LRFD) uses load and resistance factors instead of factors of safety. LRFD also establishes the geotechnical resistance much more precisely than WSD. This research establishes load and resistance factors using reliability analysis and specifically lays out methods of calculation of resistance for four limit states related to MSE wall design: overturning, sliding, reinforcement pullout and reinforcement rupture.

Geotechnical design has traditionally been done using working stress design (WSD). Working stress design identifies limit states for a structure, then protects against their occurrence using factors of safety. Load and resistance factor design (LRFD) uses load and resistance factors instead of factors of safety. LRFD also establishes the geotechnical resistance much more precisely than WSD. This research establishes load and resistance factors using reliability analysis and specifically lays out methods of calculation of resistance for four limit states related to MSE wall design: overturning, sliding, reinforcement pullout and reinforcement rupture.

Geotechnical design has traditionally been done using working stress design (WSD). Working stress design identifies limit states for a structure, then protects against their occurrence using factors of safety. Load and resistance factor design (LRFD) uses load and resistance factors instead of factors of safety. LRFD also establishes the geotechnical resistance much more precisely than WSD. This research establishes load and resistance factors using reliability analysis and specifically lays out methods of calculation of resistance for four limit states related to MSE wall design: overturning, sliding, reinforcement pullout and reinforcement rupture.

Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- semous words in context with their correct meaning, known as a sense. WSD is riddled with various obstacles that must be overcome in order to reach its full potential. One of these problems is the aspect of the representation of word meaning. Traditional WSD algorithms make the assumption that a word in a given context has only one meaning and therfore can return only one discrete sense. On the other hand, a novel approach is that a given word can have multiple senses. Studies on graded word sense assignment (Erk et al., 2009) as well as in cognitive science (Hampton, 2007; Murphy, 2002) support this theory. It has therefore been adopted in a novel, paraphrasing system which performs word sense disambiguation by returning a probability distribution over potential paraphrases (in this case synonyms) of a given word. However, it is unknown how well this type of algorithm fares against the traditional one. The current study thus examines if and how it is possible to make a comparison of the two. A method of comparison is evaluated and subsequently rejected. Reasons for this as well as suggestions for a fair and accurate comparison are presented.

Word sense disambiguation (WSD) is the process of computationally identifying and labeling poly- semous words in context with their correct meaning, known as a sense. WSD is riddled with various obstacles that must be overcome in order to reach its full potential. One of these problems is the aspect of the representation of word meaning. Traditional WSD algorithms make the assumption that a word in a given context has only one meaning and therfore can return only one discrete sense. On the other hand, a novel approach is that a given word can have multiple senses. Studies on graded word sense assignment (Erk et al., 2009) as well as in cognitive science (Hampton, 2007; Murphy, 2002) support this theory. It has therefore been adopted in a novel, paraphrasing system which performs word sense disambiguation by returning a probability distribution over potential paraphrases (in this case synonyms) of a given word. However, it is unknown how well this type of algorithm fares against the traditional one. The current study thus examines if and how it is possible to make a comparison of the two. A method of comparison is evaluated and subsequently rejected. Reasons for this as well as suggestions for a fair and accurate comparison are presented.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

This study discusses the structure of Swedish compounds within the framework of Construction Grammar, and applies the result to Word Sense Disambiguation of compound components. A construction-based approach is shown to achieve significantly better results than a set of baselines.

En el presente artículo se expone la estructura de un algoritmo para la desambiguación automática de significados a partir de colocados. El objetivo de nuestro algoritmo es lograr la máxima eficiencia reduciendo al mínimo (1) los costes computacionales y (2) el recurso a los corpus anotados o etiquetados. La formalización del algoritmo se fundamenta en el análisis de funciones discriminantes. Esta técnica estadística nos permite parametrizar cada uno de los colocados con su correspondiente significado, valiéndonos solamente del texto plano. Los datos parametrizados nos permitirán clasificar cada caso (frases con una palabra ambigua) en una variable de valores de dependientes (es decir, cada uno de los significados de la palabra ambigua). Para comprobar la validez y eficiencia de nuestro algoritmo desambiguador, desambiguamos primero manualmente el significado de la palabra estudiada en cada una de las frases en que ésta aparecía, para luego validar los datos clasificados con la aplicación automática del desambiguador de sentidos. Finalmente, presentamos los resultados globales de nuestro algoritmo, tras aplicarlo a una muestra de limitada de oraciones de ambas lenguas, español e inglés. Al mismo tiempo ponemos de relieve algunos de los aspectos que consideramos relevantes de cara a investigaciones o trabajos futuros., This paper presents an algorithm based on collocational data for word sense disambiguation (WSD). The aim of this algorithm is to maximize efficiency by minimizing (1) computational costs and (2) linguistic tagging/annotation. The formalization of our WSD algorithm is based on discriminant function analysis (DFA). This statistical technique allows us to parameterize each collocational item with its meaning, using just bare text. The parameterized data allow us to classify cases (sentences with an ambiguous word) into the values of a categorical dependent (each of the meanings of the ambiguous word). To evaluate the validity and efficiency of our WSD algorithm, we previously hand sense-tagged all the sentences containing ambiguous words and then cross-validated the hand sense-tagged data with the automatic WSD performance. Finally, we present the global results of our algorithm after applying it to a limited set of words in both languages: Spanish and English, highlighting the points we consider relevant for further analysis.

En el presente artículo se expone la estructura de un algoritmo para la desambiguación automática de significados a partir de colocados. El objetivo de nuestro algoritmo es lograr la máxima eficiencia reduciendo al mínimo (1) los costes computacionales y (2) el recurso a los corpus anotados o etiquetados. La formalización del algoritmo se fundamenta en el análisis de funciones discriminantes. Esta técnica estadística nos permite parametrizar cada uno de los colocados con su correspondiente significado, valiéndonos solamente del texto plano. Los datos parametrizados nos permitirán clasificar cada caso (frases con una palabra ambigua) en una variable de valores de dependientes (es decir, cada uno de los significados de la palabra ambigua). Para comprobar la validez y eficiencia de nuestro algoritmo desambiguador, desambiguamos primero manualmente el significado de la palabra estudiada en cada una de las frases en que ésta aparecía, para luego validar los datos clasificados con la aplicación automática del desambiguador de sentidos. Finalmente, presentamos los resultados globales de nuestro algoritmo, tras aplicarlo a una muestra de limitada de oraciones de ambas lenguas, español e inglés. Al mismo tiempo ponemos de relieve algunos de los aspectos que consideramos relevantes de cara a investigaciones o trabajos futuros., This paper presents an algorithm based on collocational data for word sense disambiguation (WSD). The aim of this algorithm is to maximize efficiency by minimizing (1) computational costs and (2) linguistic tagging/annotation. The formalization of our WSD algorithm is based on discriminant function analysis (DFA). This statistical technique allows us to parameterize each collocational item with its meaning, using just bare text. The parameterized data allow us to classify cases (sentences with an ambiguous word) into the values of a categorical dependent (each of the meanings of the ambiguous word). To evaluate the validity and efficiency of our WSD algorithm, we previously hand sense-tagged all the sentences containing ambiguous words and then cross-validated the hand sense-tagged data with the automatic WSD performance. Finally, we present the global results of our algorithm after applying it to a limited set of words in both languages: Spanish and English, highlighting the points we consider relevant for further analysis.

Este artículo presenta el resultado del estudio de un amplio conjunto de bases de conocimiento multilíngües actualmente disponibles que pueden ser de interés para un gran número de tareas de procesamiento semántico a gran escala. El estudio incluye una amplia gama de recursos derivados de forma manual y automática para el inglés y castellano. Con ello pretendemos mostrar una imagen clara de su estado actual. Para establecer una comparación justa y neutral, la calidad de cada recurso se ha evaluado indirectamente usando el mismo método en dos tareas de resolución de la ambigüedad semántica de las palabras (WSD, del inglés Word Sense Disambiguation). En concreto, las tareas de muestra léxica del inglés del Senseval-3., This report presents a wide survey of publicly available multilingual Knowledge Resources that could be of interest for wide–coverage semantic processing tasks. We also include an empirical evaluation in a multilingual scenario of the relative quality of some of these large-scale knowledge resources. The study includes a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a Word Sense Disambiguation task (Senseval-3 English Lexical Sample Task).

Este artículo presenta el resultado del estudio de un amplio conjunto de bases de conocimiento multilíngües actualmente disponibles que pueden ser de interés para un gran número de tareas de procesamiento semántico a gran escala. El estudio incluye una amplia gama de recursos derivados de forma manual y automática para el inglés y castellano. Con ello pretendemos mostrar una imagen clara de su estado actual. Para establecer una comparación justa y neutral, la calidad de cada recurso se ha evaluado indirectamente usando el mismo método en dos tareas de resolución de la ambigüedad semántica de las palabras (WSD, del inglés Word Sense Disambiguation). En concreto, las tareas de muestra léxica del inglés del Senseval-3., This report presents a wide survey of publicly available multilingual Knowledge Resources that could be of interest for wide–coverage semantic processing tasks. We also include an empirical evaluation in a multilingual scenario of the relative quality of some of these large-scale knowledge resources. The study includes a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a Word Sense Disambiguation task (Senseval-3 English Lexical Sample Task).

Este artículo presenta el resultado del estudio de un amplio conjunto de bases de conocimiento multilingües actualmente disponibles que pueden ser de interés para un gran número de tareas de procesamiento semántico a gran escala. El estudio incluye una amplia gama de recursos derivados de forma manual y automática para el inglés y castellano. Con ello pretendemos mostrar una imagen clara de su estado actual. Para establecer una comparación justa y neutral, la calidad de cada recurso se ha evaluado indirectamente usando el mismo método en dos tareas de resolución de la ambigüedad semántica de las palabras (WSD, del inglés Word Sense Disambiguation). En concreto, las tareas de muestra léxica del ingles del Senseval-3. --- This report presents a wide survey of publicly available multilingual Knowledge Resources that could be of interest for wide–coverage semantic processing tasks. We also include an empirical evaluation in a multilingual scenario of the relative quality of some of these large-scale knowledge resources. The study includes a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a Word Sense Disambiguation task (Senseval-3 English Lexical Sample Task)., Peer Reviewed, Postprint (published version)

Tesis doctoral en Informática Realizada por Manuel García Vega y dirigida por el Doctor L. Alfonso Ureña López (Univ. de Jaén). El acto de defensa de tesis tuvo lugar en Jaén en diciembre de 2006 ante el tribunal formado por los doctores Miguel Toro Bonilla (Univ. de Sevilla), Manuel Palomar Sanz (Univ. de Alicante), Lidia Moreno Boronat (Univ. Politécnica de Valencia), Andrés Montoyo Guijarro (Univ. de Alicante) y María Teresa Martín Valdivia (Univ. de Jaén). La calificación obtenida fue Sobresaliente Cum Laudem por unanimidad., PhD thesis in Computer Science written by Manuel García Vega under the supervision of Dr. L. Alfonso Ureña López (Univ. of Jaén). The author was examined in December 2006 in Jaén by the committee formed by Miguel Toro Bonilla (Univ. of Sevilla), Manuel Palomar Sanz (Univ. of Alicante), Lidia Moreno Boronat (Univ. Politécnica of Valencia), Andrés Montoyo Guijarro (Univ. of Alicante) y María Teresa Martín Valdivia (Univ. of Jaén). The grade obtained was Sobresaliente Cum Laudem.

En este trabajo se presenta un nuevo recurso, diseñado con el objetivo de ser usado en desambiguación semántica de las palabras en español, basado en las relaciones sintagmáticas entre las palabras. Las relaciones sintagmáticas son relaciones entre sentidos dentro de un sintagma o dentro de una oración. En nuestro caso, estas relaciones han sido extraídas de un corpus anotado manualmente, corpus Cast3LB. Este corpus ha sido anotado con información sintáctica (constituyentes y funciones) e información semántica (sentidos de WordNet). De este corpus se han extraído aproximadamente 3000 patrones. Cada patrón muestra la relación sintagmática entre el sentido del verbo y el sentido de sus argumentos dentro de una oración. Sin embargo, estos patrones son excesivamente específicos para ser usados en contextos multilingües o en documentos de dominio general. Para solucionar esto es necesario obtener patrones más generales semánticamente, para lo cual se ha utilizado la ontología SUMO., In this paper, we present a new resource, designed for being used in WSD, based on syntagmatic relations between senses for Spanish. These relations have been extracted from a corpus: the Cast3LB corpus which has been manually annotated with syntactic and semantic information (WordNet senses). From it, approximately 3000 patterns have been extracted. These patterns show the syntagmatic relations between verb senses and its arguments within a sentence. However, these patterns can be too specific to be used in multilingual contexts or in open domain texts. Consequently, it is necessary to obtain more abstract patterns. In order to do so, we have also developed general patterns using semantic classes based on the SUMO ontology.

En este trabajo se presenta un nuevo recurso, diseñado con el objetivo de ser usado en desambiguación semántica de las palabras en español, basado en las relaciones sintagmáticas entre las palabras. Las relaciones sintagmáticas son relaciones entre sentidos dentro de un sintagma o dentro de una oración. En nuestro caso, estas relaciones han sido extraídas de un corpus anotado manualmente, corpus Cast3LB. Este corpus ha sido anotado con información sintáctica (constituyentes y funciones) e información semántica (sentidos de WordNet). De este corpus se han extraído aproximadamente 3000 patrones. Cada patrón muestra la relación sintagmática entre el sentido del verbo y el sentido de sus argumentos dentro de una oración. Sin embargo, estos patrones son excesivamente específicos para ser usados en contextos multilingües o en documentos de dominio general. Para solucionar esto es necesario obtener patrones más generales semánticamente, para lo cual se ha utilizado la ontología SUMO., In this paper, we present a new resource, designed for being used in WSD, based on syntagmatic relations between senses for Spanish. These relations have been extracted from a corpus: the Cast3LB corpus which has been manually annotated with syntactic and semantic information (WordNet senses). From it, approximately 3000 patterns have been extracted. These patterns show the syntagmatic relations between verb senses and its arguments within a sentence. However, these patterns can be too specific to be used in multilingual contexts or in open domain texts. Consequently, it is necessary to obtain more abstract patterns. In order to do so, we have also developed general patterns using semantic classes based on the SUMO ontology.

El objetivo de este artículo es dar a conocer la metodología, los resultados y las futuras líneas de investigación que se derivan de una novedosa aproximación a la tarea de Word Sense Disambiguation (WSD). Dicha aproximación consiste, a grandes rasgos, en la construcción de clasificadores para distintas clases semánticas y su posterior combinación. De esta forma, esperamos contar no sólo con sistemas de WSD con una granularidad más gruesa que la ofrecida comúnmente por los sistemas basados en sentidos de WordNet, sino también con sistemas que ofrezcan distintas perspectivas del problema de forma que su combinación resulte beneficiosa para el resultado final de la tarea., The aim of this paper is describing the experiments, results achieved and further work, in a novel approach to the Word Sense Disambiguation (WSD) task. This novel approach consists, mainly, in the learning and combination of several semantic class classifiers. So we can not only get WSD systems with coarser granularity than the traditionally offered by WordNet senses, but also systems showing different views of the task which allows us to improve the overall task results.

En este artículo presentamos una extensión de una arquitectura integrada diseñada originalmente para Semantic Parsing a WSD. El marco propuesto permitirá que ambas tareas puedan colaborar y llevarse a cabo simultáneamente. Se ha probado la validez y robustez de esta arquitectura contra una tarea de WSD bien definida (el SENSEVAL-II English Lexical Sample) aplicando modelos sintáctico-semánticos adquiridos automáticamente., This paper presents an extension for WSD of an integrated arquitecture designed for Semantic Parsing. In the proposed framework, both tasks could be addressed simultaneously, collaborating between them. The feasibility and robustness of the proposed arquitecture have been proved against a well-defined task on WSD (the SENSEVAL-II English Lexical Sample) using automatically acquired models.

En este artículo presentamos una extensión de una arquitectura integrada diseñada originalmente para Semantic Parsing a WSD. El marco propuesto permitirá que ambas tareas puedan colaborar y llevarse a cabo simultáneamente. Se ha probado la validez y robustez de esta arquitectura contra una tarea de WSD bien definida (el SENSEVAL-II English Lexical Sample) aplicando modelos sintáctico-semánticos adquiridos automáticamente., This paper presents an extension for WSD of an integrated arquitecture designed for Semantic Parsing. In the proposed framework, both tasks could be addressed simultaneously, collaborating between them. The feasibility and robustness of the proposed arquitecture have been proved against a well-defined task on WSD (the SENSEVAL-II English Lexical Sample) using automatically acquired models.

El objetivo de este artículo es dar a conocer la metodología, los resultados y las futuras líneas de investigación que se derivan de una novedosa aproximación a la tarea de Word Sense Disambiguation (WSD). Dicha aproximación consiste, a grandes rasgos, en la construcción de clasificadores para distintas clases semánticas y su posterior combinación. De esta forma, esperamos contar no sólo con sistemas de WSD con una granularidad más gruesa que la ofrecida comúnmente por los sistemas basados en sentidos de WordNet, sino también con sistemas que ofrezcan distintas perspectivas del problema de forma que su combinación resulte beneficiosa para el resultado final de la tarea., The aim of this paper is describing the experiments, results achieved and further work, in a novel approach to the Word Sense Disambiguation (WSD) task. This novel approach consists, mainly, in the learning and combination of several semantic class classifiers. So we can not only get WSD systems with coarser granularity than the traditionally offered by WordNet senses, but also systems showing different views of the task which allows us to improve the overall task results.

La resolución de la ambigüedad léxica mejora significativamente muchas tareas del procesamiento del lenguaje natural. Presentamos un desambiguador supervisado basado en el Modelo de Espacio Vectorial en el que sus pesos se entrenan con un algoritmo competitivo basado en el modelo de Kohonen, concretamente el LVQ. Para ello, hace uso de las distintas relaciones semánticas de WordNet y también del corpus SemCor. El desambiguador se evalúa haciendo una simulación de participación en la competición SENSEVAL-2. Como muestran los resultados, la posición obtenida es muy buena., Word Sense Disambiguation improves several tasks of Natural Language Processing. We present a supervised disambiguator based on Vector Space Model, where its weights are trained with a learning vector quantization algorithm based on the Kohonen Model (LVQ algorithm) and using different semantic relations of WordNet and SemCor corpus. We also include an evaluation making a simulation of participation in SENSEVAL-2, obtaining a good position.

La resolución de la ambigüedad léxica mejora significativamente muchas tareas del procesamiento del lenguaje natural. Presentamos un desambiguador supervisado basado en el Modelo de Espacio Vectorial en el que sus pesos se entrenan con un algoritmo competitivo basado en el modelo de Kohonen, concretamente el LVQ. Para ello, hace uso de las distintas relaciones semánticas de WordNet y también del corpus SemCor. El desambiguador se evalúa haciendo una simulación de participación en la competición SENSEVAL-2. Como muestran los resultados, la posición obtenida es muy buena., Word Sense Disambiguation improves several tasks of Natural Language Processing. We present a supervised disambiguator based on Vector Space Model, where its weights are trained with a learning vector quantization algorithm based on the Kohonen Model (LVQ algorithm) and using different semantic relations of WordNet and SemCor corpus. We also include an evaluation making a simulation of participation in SENSEVAL-2, obtaining a good position.

La resolución de la ambigüedad léxica es una tarea importante dentro del procesamiento del lenguaje natural. En este trabajo, se presenta un nuevo enfoque basado en redes neuronales para aumentar la precisión en la tarea de desambiguación. Concretamente, se utiliza el algoritmo de aprendizaje basado en el modelo de Kohonen conocido con el nombre de algoritmo de aprendizaje por cuantificación vectorial LVQ (Learning Vector Quatization). Los resultados obtenidos demuestran que la utilización de dicho algoritmo competitivo supera notablemente a otros algoritmos ampliamente utilizados en otros trabajos., Word Sense Disambiguation is an important task in Natural Language Processing. This work shows a new approach based on neuronal networks that increases the precision in WSD task. The learning algorithm is based on the Kohonen model, known with the name of the LVQ algorithm (Learning Vector Quatization). The results demonstrate that the use of this competitive algorithm performes better than other algorithms widely used in other works.

La resolución de la ambigüedad léxica es una tarea importante dentro del procesamiento del lenguaje natural. En este trabajo, se presenta un nuevo enfoque basado en redes neuronales para aumentar la precisión en la tarea de desambiguación. Concretamente, se utiliza el algoritmo de aprendizaje basado en el modelo de Kohonen conocido con el nombre de algoritmo de aprendizaje por cuantificación vectorial LVQ (Learning Vector Quatization). Los resultados obtenidos demuestran que la utilización de dicho algoritmo competitivo supera notablemente a otros algoritmos ampliamente utilizados en otros trabajos., Word Sense Disambiguation is an important task in Natural Language Processing. This work shows a new approach based on neuronal networks that increases the precision in WSD task. The learning algorithm is based on the Kohonen model, known with the name of the LVQ algorithm (Learning Vector Quatization). The results demonstrate that the use of this competitive algorithm performes better than other algorithms widely used in other works.

In this paper we apply Schapire and Singer's AdaBoost.MH boosting algorithm to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar--based approaches, which represent state--of--the--art accuracy on WSD. In order to make boosting practical for a real learning domain of thousands of words we study several ways of accelerating the algorithm by reducing the feature space. The best variant, which we call LazyBoosting, is tested on a medium--large sense--tagged corpus containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmank algorithms., Postprint (published version)

In this report, some collaborative work between the fields of Machine Learning (ML) and Natural Language Processing (NLP) is presented. The document is structured in two parts. The first part includes a superficial but comprehensive survey covering the state-of-the-art of machine learning techniques applied to natural language learning tasks. In the second part, a particular problem, namely Word Sense Disambiguation (WSD), is studied in more detail. In doing so, four algorithms for supervised learning, which belong to different families, are compared in a benchmark corpus for the WSD task. Both qualitative and quantitative conclusions are drawn., Postprint (published version)

This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar--based classification, on the Word Sense Disambiguation (WSD) problem. The aim of the work is twofold. Firstly, it attempts to contribute to clarify some confusing information about the comparison between both methods appearing in the related literature. In doing so, several directions have been explored, including: testing several modifications of the basic learning algorithms and varying the feature space. Secondly, an improvement of both algorithms is proposed, in order to deal with large attribute sets. This modification, which basically consists in using only the positive information appearing in the examples, allows to improve greatly the efficiency of the methods, with no loss in accuracy. The experiments have been performed on the largest sense--tagged corpus available containing the most frequent and ambiguous English words. Results show that the Exemplar--based approach to WSD is generally superior to the Bayesian approach, especially when a specific metric for dealing with symbolic attributes is used., Postprint (author's final draft)

This report describes a set of experiments carried out to explore the portability of alternative supervised Word Sense Disambiguation algorithms. The aim of the work is threefold: firstly, studying the performance of these algorithms when tested on a different corpus from that they were trained on; secondly, exploring their ability to tune to new domains, and thirdly, demonstrating empirically that the LazyBoosting algorithm outperforms state-of-the-art supervised WSD algorithms in both previous situations., Postprint (published version)

La Cognitive Radio è un dispositivo in grado di reagire ai cambiamenti dell’ambiente radio in cui opera, modificando autonomamente e dinamicamente i propri parametri funzionali tra cui la frequenza, la potenza di trasmissione e la modulazione. Il principio di base di questi dispositivi è l’accesso dinamico alle risorse radio potenzialmente non utilizzate, con cui utenti non in possesso di licenze possono sfruttare le frequenze che in un determinato spazio temporale non vengono usate, preoccupandosi di non interferire con gli utenti che hanno privilegi su quella parte di spettro. Devono quindi essere individuati i cosiddetti “spectrum holes” o “white spaces”, parti di spettro assegnate ma non utilizzate, dai quali prendono il nome i dispositivi.Uno dei modi per individuare gli “Spectrum holes” per una Cognitive Radio consiste nel cercare di captare il segnale destinato agli utenti primari; questa tecnica è nota con il nome di Spectrum Sensing e consente di ottenere essenzialmente una misura all’interno del canale considerato al fine di determinare la presenza o meno di un servizio protetto. La tecnica di sensing impiegata da un WSD che opera autonomamente non è però molto efficiente in quanto non garantisce una buona protezione ai ricevitori DTT che usano lo stesso canale sul quale il WSD intende trasmettere.A livello europeo la soluzione che è stata ritenuta più affidabile per evitare le interferenze sui ricevitori DTT è rappresentata dall’uso di un geo-location database che opera in collaborazione con il dispositivo cognitivo.Lo scopo di questa tesi è quello di presentare un algoritmo che permette di combinare i due approcci di geo-location database e Sensing per definire i livelli di potenza trasmissibile da un WSD.