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This article introduces the Children and Young People’s Books-Lexicon (CYP-LEX), a large-scale lexical database derived from books popular with children and young people in the United Kingdom. CYP-LEX includes 1,200 books evenly distributed across three age bands (7–9, 10–12, 13+) and comprises over 70 million tokens and over 105,000 types. For each word in each age band, we provide its raw and Zipf-transformed frequencies, all parts-of-speech in which it occurs with raw frequency and lemma for each occurrence, and measures of count-based contextual diversity. Together and individually, the three CYP-LEX age bands contain substantially more words than any other publicly available database of books for primary and secondary school children. Most of these words are very low in frequency, and a substantial proportion of the words in each age band do not occur on British television. Although the three age bands share some very frequent words, they differ substantially regarding words that occur less frequently, and this pattern also holds at the level of individual books. Initial analyses of CYP-LEX illustrate why independent reading constitutes a challenge for children and young people, and they also underscore the importance of reading widely for the development of reading expertise. Overall, CYP-LEX provides unprecedented information into the nature of vocabulary in books that British children aged 7+ read, and is a highly valuable resource for those studying reading and language development.

A novel lexical resource for treating speech impairments from childhood to senility: DILLo—Database Italiano del Lessico per Logopedisti (i.e. Italian Database for Speech-Language Pathologists) is presented. DILLo is a free online web application that allows extraction of filtered wordlists for flexible rehabilitative purposes. Its major aim is to provide Italian speech-language pathologists (SLPs) with a resource that takes advantage of Information and Communication Technologies for language in a healthcare setting. DILLo’s design adopts an integrated approach that envisages fruitful cooperation between clinical and linguistic professionals. The 7690 Italian words in the database have been selected based on phonological, phonotactic, and morphological properties, and their frequency of use. These linguistic features are encoded in the tool, which includes the orthographic and phonological transcriptions, and the phonotactic structure of each word. Moreover, most of the entries are associated with their respective ARASAAC pictogram, providing an additional and inclusive tool for treating speech impairments. The user-friendly interface is structured to allow for different and adaptable search options. DILLo allows Speech-Language Pathologists (SLPs) to obtain a rich, tailored, and varied selection of suitable linguistic stimuli. It can be used to customize the treatment of many impairments, e.g. Speech Sound Disorders, Childhood Apraxia of Speech, Specific Learning Disabilities, aphasia, dysarthria, dysphonia, and the auditory training that follows cochlear implantations., SCOPUS: ar.j, info:eu-repo/semantics/published

Our study aims to explore how much information about areal patterns of colexification we can gain from lexical databases, such as CLICS and ASJP. We adopt a bottom-up (rather than hypothesis-driven) approach, identifying areal patterns in three steps: (i) determine spatial autocorrelations in the data, (ii) identify clusters as candidates for convergence areas and (iii) test the clusters resulting from the second step controlling for genealogical relatedness. Moreover, we identify a (genealogical) diversity index for each cluster. This approach yields promising results, which we regard as a proof of concept, but we also point out some drawbacks of the use of major lexical databases.

Published: 12 November 2018

Published: 12 November 2018

Published: 12 November 2018

Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic information, such as sense and syntactic category, information that, not only aids the process of detection, but also allows us to expand the cyberbullying database using WordNet (Miller, 1993).

Research has demonstrated that it is challenging for English as an Additional Language (EAL) writers to acquire phraseological competence in academic English and develop a good working knowledge of discipline-specific formulaic language. This paper aims to explore if SciE-Lex, a powerful lexical database of biomedical research articles, can be exploited by EAL writers to enhance their command of formulaic language in biomedical English published writing. Our paper builds on the challenges associated with formulaic language (namely collocations) for EAL writers, it reflects on the benefits of using a lexical database and it evaluates a pedagogical approach to helping EAL writers produce publishable texts. It specifically highlights results from two writing workshops conducted for EAL writers (medical researchers in the present study). The workshops involved medical researchers working on drafts of their writing using SciE-Lex. Our paper reports on the specific benefits of using SciE-Lex as demonstrated by revisions in the writing produced by the EAL medical researchers. This paper aims to contribute to current discussion on English for Research Publication Purposes (ERPP) for the EAL community who now form the main contributors to research knowledge dissemination, La investigación ha demostrado que el uso del inglés como lengua adicional (English as an Additional Language, EAL por sus siglas en inglés) en la escritura académica representa un reto para los investigadores no nativos de dicha lengua, en tanto que estos deben adquirir competencia fraseológica en inglés académico y desarrollar un conocimiento del lenguaje formulaico propio de la disciplina. Este artículo busca explorar si la base de datos de artículos de investigación biomédicos SciE-Lex puede utilizarse por estos escritores para mejorar su dominio del lenguaje formulaico en la escritura biomédica en lengua inglesa. En este trabajo se describen los retos asociados al lenguaje formulaico (las llamadas combinaciones de palabras) a los que se enfrentan los escritores de EAL y valora los beneficios pedagógicos de la utilización de esta base de datos léxica como apoyo a la producción de textos publicables. En concreto, se describen dos talleres de escritura diseñados para escritores de EAL (investigadores del ámbito de la medicina en el presente estudio). En los talleres estos investigadores trabajaron sobre varios borradores de textos utilizando SciE-Lex. Se describen los beneficios de su uso a través de las revisiones de los textos que llevaron a cabo los investigadores. El presente trabajo busca contribuir al debate actual sobre el llamado English for Research Publication Purposes (ERPP, por sus siglas en inglés) en la comunidad de escritores de EAL, comunidad que juega un papel primordial en la difusión del conocimiento científico.

Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj, similarityij). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy. © Springer International Publishing AG 2017.

Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic information, such as sense and syntactic category, information that, not only aids the process of detection, but also allows us to expand the cyberbullying database using WordNet (Miller, 1993).

Research has demonstrated that it is challenging for English as an Additional Language (EAL) writers to acquire phraseological competence in academic English and develop a good working knowledge of discipline-specific formulaic language. This paper aims to explore if SciE-Lex, a powerful lexical database of biomedical research articles, can be exploited by EAL writers to enhance their command of formulaic language in biomedical English published writing. Our paper builds on the challenges associated with formulaic language (namely collocations) for EAL writers, it reflects on the benefits of using a lexical database and it evaluates a pedagogical approach to helping EAL writers produce publishable texts. It specifically highlights results from two writing workshops conducted for EAL writers (medical researchers in the present study). The workshops involved medical researchers working on drafts of their writing using SciE-Lex. Our paper reports on the specific benefits of using SciE-Lex as demonstrated by revisions in the writing produced by the EAL medical researchers. This paper aims to contribute to current discussion on English for Research Publication Purposes (ERPP) for the EAL community who now form the main contributors to research knowledge dissemination, La investigación ha demostrado que el uso del inglés como lengua adicional (English as an Additional Language, EAL por sus siglas en inglés) en la escritura académica representa un reto para los investigadores no nativos de dicha lengua, en tanto que estos deben adquirir competencia fraseológica en inglés académico y desarrollar un conocimiento del lenguaje formulaico propio de la disciplina. Este artículo busca explorar si la base de datos de artículos de investigación biomédicos SciE-Lex puede utilizarse por estos escritores para mejorar su dominio del lenguaje formulaico en la escritura biomédica en lengua inglesa. En este trabajo se describen los retos asociados al lenguaje formulaico (las llamadas combinaciones de palabras) a los que se enfrentan los escritores de EAL y valora los beneficios pedagógicos de la utilización de esta base de datos léxica como apoyo a la producción de textos publicables. En concreto, se describen dos talleres de escritura diseñados para escritores de EAL (investigadores del ámbito de la medicina en el presente estudio). En los talleres estos investigadores trabajaron sobre varios borradores de textos utilizando SciE-Lex. Se describen los beneficios de su uso a través de las revisiones de los textos que llevaron a cabo los investigadores. El presente trabajo busca contribuir al debate actual sobre el llamado English for Research Publication Purposes (ERPP, por sus siglas en inglés) en la comunidad de escritores de EAL, comunidad que juega un papel primordial en la difusión del conocimiento científico.

[EN] The aim of this article is to devise the method of lemmatisation of strong verbs from a corpus of Old English with a view to maximising the automatic search for the inflectional forms, with the corresponding minimisation of manual revision of the verbs under analysis. The search algorithm, which consists of query strings and filters, is launched on the lemmatiser Norna, a component of the lexical database of Old English Nerthus. The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms., [ES] El objetivo del presente artículo es idear un método de lematización de verbos fuertes de inglés antiguo, con el propósito de maximizar la búsqueda automática de formas flexivas, con la correspondiente reducción en la revisión manual de los verbos en estudio. El algoritmo de búsqueda consiste en cadenas de búsqueda y filtros, ejecutados en el lematizador Norna, un componente de la base de datos léxica de inglés antiguo Nerthus. Las conclusiones del artículo insisten en los límites de la lematización automática, así como en las posibilidades de refinamiento del método de lematización para acomodar las formas menos predecibles.

[EN] The aim of this article is to devise the method of lemmatisation of strong verbs from a corpus of Old English with a view to maximising the automatic search for the inflectional forms, with the corresponding minimisation of manual revision of the verbs under analysis. The search algorithm, which consists of query strings and filters, is launched on the lemmatiser Norna, a component of the lexical database of Old English Nerthus. The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms., [ES] El objetivo del presente artículo es idear un método de lematización de verbos fuertes de inglés antiguo, con el propósito de maximizar la búsqueda automática de formas flexivas, con la correspondiente reducción en la revisión manual de los verbos en estudio. El algoritmo de búsqueda consiste en cadenas de búsqueda y filtros, ejecutados en el lematizador Norna, un componente de la base de datos léxica de inglés antiguo Nerthus. Las conclusiones del artículo insisten en los límites de la lematización automática, así como en las posibilidades de refinamiento del método de lematización para acomodar las formas menos predecibles.

[EN] The aim of this article is to devise the method of lemmatisation of strong verbs from a corpus of Old English with a view to maximising the automatic search for the inflectional forms, with the corresponding minimisation of manual revision of the verbs under analysis. The search algorithm, which consists of query strings and filters, is launched on the lemmatiser Norna, a component of the lexical database of Old English Nerthus. The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms., [ES] El objetivo del presente artículo es idear un método de lematización de verbos fuertes de inglés antiguo, con el propósito de maximizar la búsqueda automática de formas flexivas, con la correspondiente reducción en la revisión manual de los verbos en estudio. El algoritmo de búsqueda consiste en cadenas de búsqueda y filtros, ejecutados en el lematizador Norna, un componente de la base de datos léxica de inglés antiguo Nerthus. Las conclusiones del artículo insisten en los límites de la lematización automática, así como en las posibilidades de refinamiento del método de lematización para acomodar las formas menos predecibles.

[EN] The aim of this article is to devise the method of lemmatisation of strong verbs from a corpus of Old English with a view to maximising the automatic search for the inflectional forms, with the corresponding minimisation of manual revision of the verbs under analysis. The search algorithm, which consists of query strings and filters, is launched on the lemmatiser Norna, a component of the lexical database of Old English Nerthus. The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms., [ES] El objetivo del presente artículo es idear un método de lematización de verbos fuertes de inglés antiguo, con el propósito de maximizar la búsqueda automática de formas flexivas, con la correspondiente reducción en la revisión manual de los verbos en estudio. El algoritmo de búsqueda consiste en cadenas de búsqueda y filtros, ejecutados en el lematizador Norna, un componente de la base de datos léxica de inglés antiguo Nerthus. Las conclusiones del artículo insisten en los límites de la lematización automática, así como en las posibilidades de refinamiento del método de lematización para acomodar las formas menos predecibles.

[EN] The aim of this article is to devise the method of lemmatisation of strong verbs from a corpus of Old English with a view to maximising the automatic search for the inflectional forms, with the corresponding minimisation of manual revision of the verbs under analysis. The search algorithm, which consists of query strings and filters, is launched on the lemmatiser Norna, a component of the lexical database of Old English Nerthus. The conclusions of the article insist on the limits of automatic lemmatisation as well as the paths of refinement of the lemmatisation method in order to accomodate less predictable forms., [ES] El objetivo del presente artículo es idear un método de lematización de verbos fuertes de inglés antiguo, con el propósito de maximizar la búsqueda automática de formas flexivas, con la correspondiente reducción en la revisión manual de los verbos en estudio. El algoritmo de búsqueda consiste en cadenas de búsqueda y filtros, ejecutados en el lematizador Norna, un componente de la base de datos léxica de inglés antiguo Nerthus. Las conclusiones del artículo insisten en los límites de la lematización automática, así como en las posibilidades de refinamiento del método de lematización para acomodar las formas menos predecibles.

The «Glossarium Mediae Latinitatis Cataloniae» (GMLC) is a lexicographical project launched in the 1950s by Professor M. Bassols de Climent. Directed originally by Joan Bastardas and currently by Pere J. Quetglas, the project is now based at the Milà i Fontanals Institution of the CSIC (Spanish National Research Council), and is the result of cooperation between the Department of Classics at the University of Barcelona and the Classics Section of the institution’s Department of Historical Sciences/Mediaeval Studies. As suggested in its subheading (“Latin and Romance words documented in Catalan sources from 800 AD to 1100 AD”), the GMLC’s aim is to aid comprehension of the notarial documents and texts written in Catalonia between the 9th and the 12th centuries, which provide written testimony of the mediaeval Latin language and the development of the new Romance language. Work on the GMLC currently focuses on two complementary and closely connected areas: firstly, the writing of the texts which form the basis for the glossary, and secondly, their digitization. The common aim of these two focuses is to continue building the GMLC using both the pre-existing records compiled manually and the digitized document database, which is regularly updated. However, the materials are not intended for internal use alone; our aim is to make this large lexical database of the documents of 9th-12th century Catalonia available for public consultation. Together the GMLC lexical database and its query service make up CODOLCAT, «Corpus Documentale Latinum Cataloniae».

The creation of reusable lexical database files, based in fieldwork or arising from historical research, benefits from conformance to established standards which then greatly increases the enduring usability of the lexicon, and its later ability to link to external objects, including media. All linguistic analysis benefits from the close relationship between primary recordings and a textual corpus, but a dictionary can also benefit from links to media in the use of playable example sentences and citation forms of headwords. In this paper several examples will be used to illustrate that not all linguists want to deal with the tools required to take advantage of these methods, so, in some cases, they are better off seeking advice and assistance in advance of building the database or in its later conversion to output formats.

Este artículo presenta un método de identificación y clasificación de la valencia y las emociones presentes en un texto. Para ello, se introduce un nuevo concepto denominado disparador de emoción. Inicialmente, se construye de forma incremental una base de datos léxica de disparadores de emoción asociados a la cultura con la que se quiere trabajar, basándose en tres teorías diferentes: la Teoría de la Relevancia de Pragmática, la Teoría de la Motivación de Maslow de Psicología y la Teoría de Necesidades de Neef de Economía. La base de datos creada parte de un conjunto inicial de términos y es ampliada con la información de otros recursos léxicos, como WordNet, NomLex y dominios relevantes. El enlace entre idiomas se hace por medio de EuroWordNet y se completa y adapta a diversas culturas con bases de conocimiento específicas para cada lengua. También, se demuestra cómo la base de datos construida puede ser utilizada para buscar en textos la valencia (polaridad) y el significado afectivo. Finalmente, se evalúa el método utilizando los datos de prueba de la tarea nº 14 de Semeval “Texto afectivo” y su traducción al español. Los resultados y las mejoras se presentan junto con una discusión en la que se tratan los puntos fuertes y débiles del método y las directrices para el trabajo futuro., This paper presents a method to automatically spot and classify the valence and emotions present in written text, based on a concept we introduced - of emotion triggers. The first step consists of incrementally building a culture dependent lexical database of emotion triggers, emerging from the theory of relevance from pragmatics, Maslow´s theory of human needs from psychology and Neef´s theory of human needs in economics. We start from a core of terms and expand them using lexical resources such as WordNet, completed by NomLex, sense number disambiguated using the Relevant Domains concept. The mapping among languages is accomplished using EuroWordNet and the completion and projection to different cultures is done through language-specific commonsense knowledge bases. Subsequently, we show the manner in which the constructed database can be used to mine texts for valence (polarity) and affective meaning. An evaluation is performed on the Semeval Task No. 14: Affective Text test data and their corresponding translation to Spanish. The results and improvements are presented together with an argument on the strong and weak points of the method and the directions for future work.

Este artículo presenta un método de identificación y clasificación de la valencia y las emociones presentes en un texto. Para ello, se introduce un nuevo concepto denominado disparador de emoción. Inicialmente, se construye de forma incremental una base de datos léxica de disparadores de emoción asociados a la cultura con la que se quiere trabajar, basándose en tres teorías diferentes: la Teoría de la Relevancia de Pragmática, la Teoría de la Motivación de Maslow de Psicología y la Teoría de Necesidades de Neef de Economía. La base de datos creada parte de un conjunto inicial de términos y es ampliada con la información de otros recursos léxicos, como WordNet, NomLex y dominios relevantes. El enlace entre idiomas se hace por medio de EuroWordNet y se completa y adapta a diversas culturas con bases de conocimiento específicas para cada lengua. También, se demuestra cómo la base de datos construida puede ser utilizada para buscar en textos la valencia (polaridad) y el significado afectivo. Finalmente, se evalúa el método utilizando los datos de prueba de la tarea nº 14 de Semeval “Texto afectivo” y su traducción al español. Los resultados y las mejoras se presentan junto con una discusión en la que se tratan los puntos fuertes y débiles del método y las directrices para el trabajo futuro., This paper presents a method to automatically spot and classify the valence and emotions present in written text, based on a concept we introduced - of emotion triggers. The first step consists of incrementally building a culture dependent lexical database of emotion triggers, emerging from the theory of relevance from pragmatics, Maslow´s theory of human needs from psychology and Neef´s theory of human needs in economics. We start from a core of terms and expand them using lexical resources such as WordNet, completed by NomLex, sense number disambiguated using the Relevant Domains concept. The mapping among languages is accomplished using EuroWordNet and the completion and projection to different cultures is done through language-specific commonsense knowledge bases. Subsequently, we show the manner in which the constructed database can be used to mine texts for valence (polarity) and affective meaning. An evaluation is performed on the Semeval Task No. 14: Affective Text test data and their corresponding translation to Spanish. The results and improvements are presented together with an argument on the strong and weak points of the method and the directions for future work.

El objetivo del proyecto Portal da Língua Portuguesa es construir, con un doble objetivo, un juego de recursos léxicos. En primer lugar, estos recursos sirven como fuente de información para una página web sobre la lengua portuguesa para el público en general. En segundo lugar, son un repositorio de información léxica para la investigación lingüística. El dibujo de la base de datos es modular y relacional, y se hizo de modo que proporcione soluciones estructurales para problemas léxicos, como son los de la homonimia, variación ortográfica, etc., The goal of the Portal da Língua Portuguesa project is to construe a set of lexical resources with a double objective. On the one hand, the resources serve as the content source for a web site about the Portuguese language, aimed at the general public. On the other hand, the resources are built to serve as an open source repository of lexical information for linguistic research. The design of the database is modular and relational, and is set-up in such a way that it provides structural solutions for lexical difficulties like homonymy, orthographic variation, etc.

El objetivo del proyecto Portal da Língua Portuguesa es construir, con un doble objetivo, un juego de recursos léxicos. En primer lugar, estos recursos sirven como fuente de información para una página web sobre la lengua portuguesa para el público en general. En segundo lugar, son un repositorio de información léxica para la investigación lingüística. El dibujo de la base de datos es modular y relacional, y se hizo de modo que proporcione soluciones estructurales para problemas léxicos, como son los de la homonimia, variación ortográfica, etc., The goal of the Portal da Língua Portuguesa project is to construe a set of lexical resources with a double objective. On the one hand, the resources serve as the content source for a web site about the Portuguese language, aimed at the general public. On the other hand, the resources are built to serve as an open source repository of lexical information for linguistic research. The design of the database is modular and relational, and is set-up in such a way that it provides structural solutions for lexical difficulties like homonymy, orthographic variation, etc.

Several studies on auditory word recognition indicate that word processing is influenced by phonological similarity with other words. We describe a lexical database, VoColex, which provides several statistical indexes of phonological similarity between French words. Phonological similarity is computed according to two distinct principles. According to the first principle, phonologically similar words share initial phonemes with the target word. According to the second principle, phonological neighbours correspond to any words which can be derived from the target by a single phoneme change (substitution, addition, or deletion) whatever the position of the modified phoneme. The statistical data provided by VoCoLex allow the control and the empirical manipulation of various measures of phonological similarity, as well as quantitative descriptions of the auditory lexicon., SCOPUS: ar.j, info:eu-repo/semantics/published

Several studies on auditory word recognition indicate that word processing is influenced by phonological similarity with other words. We describe a lexical database, VoColex, which provides several statistical indexes of phonological similarity between French words. Phonological similarity is computed according to two distinct principles. According to the first principle, phonologically similar words share initial phonemes with the target word. According to the second principle, phonological neighbours correspond to any words which can be derived from the target by a single phoneme change (substitution, addition, or deletion) whatever the position of the modified phoneme. The statistical data provided by VoCoLex allow the control and the empirical manipulation of various measures of phonological similarity, as well as quantitative descriptions of the auditory lexicon., SCOPUS: ar.j, info:eu-repo/semantics/published

The practical outcome of the project,'Analysis of the Organization of Lexical Memory', is an electronic lexical database called WordNet that can be incorporated into computer systems for processing English text. WordNet includes approximately 45,000 lexicalized concepts, providing a coverage equivalent to a handheld dictionary. The database has three components, one each for nouns, verbs, and adjectives. The semantic relations that organize each component are different, but in general a lexicalized concept is represented by a set of synonyms that can be used to express the concept, the familiar semantic relations are presented by labeled pointers between synonym sets. In order to create the database, programs were written to write and edit lexical files, to convert lexical files into a database, to search the database, to strip inflections from search requests, and to display retrieved information for a user.

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému., With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému., With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému., With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému., With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému., With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.