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Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous, 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.

One of the main issues in the present and upcoming decades is the improvement of natural resource use efficiencies. The industrial use of water is preferred as a target for the implementation of sustainable models of water management with the purpose of reducing the use of this resource and generates less waste. This study aims the application of the Water Source Diagram (WSD) method in paper industry, typology associated to high wastewater generation, as a potential strategy to achieve sustainable industrial processes through wastewater reuse. Operational data from paper industry were provided by previous works. Two scenarios of freshwater consumption were generated to model the system. Both cases showed that the freshwater consumption may be reduced over 30.22%, in relation to the baseline of no reuse. The efficiency of the WSD was also compared to other results reported in the literature. Although economic evaluation has not been addressed in this work, the method could be considered as a strategic tool for decision making. Uno de los principales problemas de la presente y las próximas décadas es la mejora de la eficiencia en el uso de los recursos naturales. Se prefiere el uso industrial del agua como objetivo para la implementación de modelos sostenibles de gestión del agua con el propósito de reducir el uso de este recurso y generar menos residuos. Este estudio tiene como objetivo la aplicación del método Diagrama de fuente de agua (DFA) en la industria papelera, tipología asociada a alta generación de aguas residuales, como una estrategia potencial para lograr procesos industriales sostenibles a través de la reutilización de aguas residuales. Los datos operativos de la industria del papel fueron proporcionados por trabajos anteriores. Se generaron dos escenarios de consumo de agua dulce para modelar el sistema. Ambos casos mostraron que el consumo de agua dulce puede reducirse más del 30,22%, en relación con la línea de base de no reutilización. La eficiencia del DFA también se comparó con otros resultados reportados en la literatura. Si bien la evaluación económica no se ha abordado en este trabajo, el método podría considerarse como una herramienta estratégica para la toma de decisiones. Um dos principais problemas nas décadas atuais e nas próximas é a melhoria da eficiência do uso dos recursos naturais. O uso industrial da água é preferido como alvo para a implantação de modelos sustentáveis de gestão da água com o objetivo de reduzir o uso desse recurso e gerar menos resíduos. Este estudo visa à aplicação do método Diagrama de Fontes de Água (DFA) na indústria de papel, tipologia associada à alta geração de efluentes, como uma estratégia potencial para alcançar processos industriais sustentáveis por meio do reuso de efluentes. Os dados operacionais da indústria de papel foram fornecidos por trabalhos anteriores. Dois cenários de consumo de água doce foram gerados para modelar o sistema. Ambos os casos mostraram que o consumo de água doce pode ser reduzido em mais de 30,22%, em relação à linha de base de não reutilização. A eficiência do DFA também foi comparada com outros resultados relatados na literatura. Embora a avaliação econômica não tenha sido abordada neste trabalho, o método pode ser considerado uma ferramenta estratégica para a tomada de decisões.

We explore human judgments on how well individual patterns of 29 target verbs from the Pattern Dictionary of English Verbs describe their random KWICs. We focus on cases where more than one pattern is judged as highly appropriate for a given KWIC and seek to estimate the effect of event participants (arguments) being denotatively similar in two patterns, considering all pair combinations in a given lemma. We compare this effect to the effect of several contextual features of the KWICs, the effect of paired PDEV implicatures implying each other, and the effect of belonging to a given lemma. We show that the lemma effect is still stronger than any feature going across lemmas we have examined so far, so that each verb appears to be a little universe in its own right.

In this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian.; Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétons.

In this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian.; Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétons.

The article presents the summary of a dataset related to the risks factors of white spot disease (WSD) of farmed shrimp (Penaeus monodon) in Khulna, Bagerhat and Satkhira districts of Bangladesh. This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools were applied to get the conceptual framework for data collection regarding lists of farmers and the variables of the risk factors of WSD. In the second step, sampling of farmers, google featured questionnaire development, and mobile phone-assisted survey were carried out. The total surveyed farms were 233 consisting of 21 and 212 semi-intensive and extensive farms, respectively. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains some basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article.

The problem of word sense disambiguation (WSD) is considered in the article. Given a set of synonyms (synsets) and sentences with these synonyms. It is necessary to select the meaning of the word in the sentence automatically. 1285 sentences were tagged by experts, namely, one of the dictionary meanings was selected by experts for target words. To solve the WSD-problem, an algorithm based on a new method of vector-word contexts proximity calculation is proposed. In order to achieve higher accuracy, a preliminary epsilon-filtering of words is performed, both in the sentence and in the set of synonyms. An extensive program of experiments was carried out. Four algorithms are implemented, including a new algorithm. Experiments have shown that in a number of cases the new algorithm shows better results. The developed software and the tagged corpus have an open license and are available online. Wiktionary and Wikisource are used. A brief description of this work can be viewed in slides (https://goo.gl/9ak6Gt). Video lecture in Russian on this research is available online (https://youtu.be/-DLmRkepf58)., Comment: 15 pages, 1 table, 15 figures, accepted in the journal Transactions of Karelian Research Centre of the Russian Academy of Sciences

Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical ResourcesLexical 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.

Identifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation(WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers. Bir kelimenin geçtiği bağlam içindeki anlamını belirlemek , doğal dil işleme alanında, zorlu ve çokça uygulaması olan bir problemdir. Bu problemin literatürdeki bilinen adı, kelime belirsizlik gidermedir. Bir çok yayın İngiliz dili ve verileri üzerine yoğunlaşmış çalışmalardır. Bu çalışmada kullandığımız veri kümesi, Penn Treebank Corpus'dan derlenmiş ve Türkçe'ye çevrilmiş 1400 cümleden oluşmaktadır. Çalışmamızın amacı 6 farklı öznitelik çıkarım algoritmasının performanslarını farklı sınıflandırıcılarla ölçmektir. Kullandığımız sınıflandırma algoritmaları, C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear ve multilayer Perceptron'dır. yayınımızın amacı açıklanan özniteliklerin morfolojik açıdan zengin olan bir dilde (Türkçe), farklı sınıflandırıcılarla verdiği performans ölçmektir. Publisher's Version

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.

Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at http://trainomatic.org.

Display Omitted HighlightsObjective: to develop a method that can disambiguate terms in biomedical text.WSD method exploits similarity and relatedness information extracted from the UMLS.Evaluates the efficacy of similarity and relatedness measures on WSD.IC-based measures obtains higher accuracy than path-based and relatedness measures. IntroductionIn this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD. MethodWe evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. ResultsWe show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset. AvailabilityThe WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.

The term of word sense disambiguation, WSD, is introduced in the context of text document processing. A knowledge based approach is conducted using WordNet lexical ontology, describing its structure and components used for the process of identification of context related senses of each polysemy words. The principal distance measures using the graph associated to WordNet are presented, analyzing their advantages and disadvantages. A general model for aggregation of distances and probabilities is proposed and implemented in an application in order to detect the context senses of each word. For the non-existing words from WordNet, a similarity measure is used based on probabilities of co-occurrences. The module of WSD is proposed for integration in the step of processing documents such as supervised and unsupervised classification in order to maximize the correctness of the classification. Future work is related to the implementation of different domain oriented ontologies.Keywords: WSD, Similarity Measure, WordNet, Ontology, Synset(ProQuest: ... denotes formulae omitted.)1 IntroductionFor the acquisition of knowledge in artificial intelligence, two approaches defined in [1] are used:* transfer process between human to knowledge base, process with a major disadvantage given by the fact that the one who has knowledge cannot easily identify it;* conceptual modeling process by building models in which are placed the new knowledge as they are acquired, this process leading to the appearance of the ontology as a systematic organization of knowledge, data of the reality, leading to the construction of theories upon what it exists.An essential role of ontology is to be reused in multiple applications. Mapping two or more ontologies is called alignment. This task is particularly difficult, the main cause of limitation in extending existing ontologies [1].Direction that follows the ontology is supported by the introduction of artificial intelligence techniques to emulate the mental representation of concepts used, and the interpenetration of these links.The kernel of the ontology is defined as a system 0 = (£, T, C*,dC, ROOT), where:* £ is the lexicon formed out of the terms from the natural language;* C* a set of concepts;* T represents the reference function that maps the set of terms of the lexicon to the set of concepts;* H is the hierarchy of the taxonomy given by the direct, acyclic, transitive and reflexive relation;* ROOT is the starting point upon which the hierarchy is built on.There are two types of ontologies as defined in [1], depending on the area in which they are used:* ontologies for knowledge-based systems are characterized by a relatively small number of concepts, but linked by a large and varied relationships, concepts are grouped into complex conceptual schemes or scenarios and for each concept there can be one or more customizations;* lexicalized ontologies, including a large number of concepts linked by a small number of relationships, like WordNet ontology concepts that are represented by sets of synonymous words, these ontologies are used in human language processing systems.It is introduced the concept of ontology as a knowledge base in the classification of documents, in order to analyze semantic documents by solving the ambiguity of the terms.This integration results in an improvement in the objective function defined for classification techniques used. The main components of an ontology are described, the concepts and relations between them. These components are analyzed, identifying methods of extracting knowledge from within.With the defined relationships between concepts it is created the graph representation seen as a taxonomy of belonging such as "is- a" of the concepts to the more general ones. The senses of a concept are defined, along with the possibility of graph representation of each sense. …

This master thesis investigated automatic methods of Word Sense Disambiguation (WSD) in HTML pages. The hypothesis was that HTML documents provide various disambiguation cues which are not normally present in general text, and which can enhance the quality of WSD. We tested several existing natural language processing toolkits which provide general WSD services, and compared these to our novel algorithms which were designed to take advantage of the HTML cues. The findings showed that our new algorithms outperformed state of the art general WSD implementations. In addition, our algorithm could provide a ranked list of potential disambiguations, which is useful in an example use case where users “tag” key words in a web page with the help of the disambiguating algorithm. INFO390 MASV-INFO

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. Iran had good experiences on shrimp farming during tow decades. White spot disease collapse the shrimp farming activities in recent years. Although goater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Five semi-intensive shrimp farms were selected in bahookalat chabahar area (2500 ha). The farms were situated at goater area. Tree ponds from each farm at random were selected for the study. All major environmental parameters such as O2, temperature, salinity. PH, nitrogen were recognized by standard method. Logistic regression were used for relationship of water parameters with occurrence of white spot disease. There were no significant relationship between PH, salinity and nitrogen in ponds and canals. But significant variations were recorded for oxygen (1.58) Temperature (0.89) with occurrence of white spot disease. Pond aeration can use for reduction and prevention of diseases.

For the first time white spot disease (WSD) was reported in shrimp farms of khoozestan province, in southwest of IRAN in 2002. Then in 2005 the neighbor province, boushehr, was contaminated. In 2008 WSD outbreak reported in sistan-bloochestan province in southeast of Iran. In 2015 all of southern shrimp farms of country except Hormozgan, the middle southern province, which has remained free of WSD, are being contaminated. White Spot disease suspended shrimp culture in thousands hectares of shrimp farms. Considering that white spot disease has not been observed in Hormozgan province yet, the question is; to what extent environmental and management factors participated in preventing WSD outbreak or cause WSD outbreak. In this study (20102012), the effects of environmental factors and management, stressors that decrease immune system function of shrimp are discussed. In addition, the role of pathogen as the main factor of outbreak is discussed. The goal of this study is to define environmental parameters and management practices associates with outbreak of white spot disease in affected provinces and discover reasons of being Hormozgan province free of this disease. In this study the role of the local environmental factors and management practice stressors in susceptibility to WSD was determine. Both the effects of environmental factors in water of ponds including total ammonia, nitrogen, dissolved oxygen, pH, salinity, transparency, and temperature and management issues related to biosecurity are studied. There were overlaps on physical and chemical parameter values obtained in clear areas with contaminated areas .Results of the data analysis suggest that lack of association with WSD incidence was 7 times greater than WSD incidence despite of disease outbreak in sistan-bloochestan province, so other sources of white spot disease virus incidence was suspected in affected areas. Histopathological examinations and polymerase chain reaction (PCR) tests during project performance did not reveal white spot disease virus evidences in post larvae examined from khoozestan province stocked in farms but disease outbreak was happened in that farms , so we suspected to management practice include feed , pond preparation and carrier of disease . Recorded values of temperature and salinity in some months during inspection in Hormozgan province specified stressful condition that may lead to WSD outbreak, however the disease did not appear. Therefore the hypothesis that the water physical and chemical conditions are reasons to prevent disease outbreak in Hormozgan province is being rejected. The policy of Hormozgan’s fishery authorities, to replaced Fenneropenaeus indicus with specific pathogen free Litopenaeus vannamei, that is more resistant to some of diseases, before incidence of WSD in farms and to before being endemic in the Hormozgan province, made an advantage compare to affected southern provinces that introduced Litopenaeus vannamei after WSD prevalence to their farms. However it does not guarantee to maintain current trend of being Hormozgan province farms free of white spot disease. Therefore establishing the principals of biosecurity are strongly emphasized. Strategies taken by the proficient authorities in preparation of SPF shrimp broodstock can be the most important factor in preventing WSD. Regarding biosecurity principals purchased feed must be free of shrimp head powder. Construction the new shrimp farms should be as far as it could be away from contaminated areas. Iranian Fisheries Science Research Institute Published

Decline in capture fisheries and sea food demand lead to improve shrimp aquaculture activities. There are valuable capacities for shrimp culture in Guater site in Chabahar which could be mentioned as making employment. White spot disease collapses the shrimp farming activities in recent years. Although Guater were the main site for shrimp culture but this site were affected by white spot disease (WSD). Environmental stressors were the main criteria for attention in this regard. An investigation was carried out to monitor management practices and to find out whether there is any relationship with occurrence of white spot disease and environmental parameters. Desirable climate fluctuation happened in monsoon seasons in Guater area parallel with shrimp culture. Evidence of thermal shock beside presence of pathogens makes suitable condition for evidence of disease. Therefor one of the most important embarks is reducing the risk of this accident by selecting suitable time for post larvae stocking. Based on our study the best time for this purpose in Guater site is from March to June .Other recommended footsteps in relation to reduce outbreaks are: settlement bio security, using immune system simulators, embrace SPF brood stocks.