Relevant bibliographies by topics / Semantic classification of nouns

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Academic literature on the topic 'Semantic classification of nouns'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Semantic classification of nouns"

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Abubakari, Hasiyatu. "Noun class system of Kusaal." Studies in African Linguistics 50, no. 1 (April 27, 2021): 116–39. http://dx.doi.org/10.32473/sal.v50i1.128792.

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It is common knowledge that noun classes in Mabia (Gur) languages are primarily characterized by stems and affixes. Common to all studies on nominal classification in Kusaal is the observation that nouns that exhibit common morphological properties also share identical semantic features. Though this is true to some extent, the generalization breeds a lot of leakages because classifications based on semantic field alone is unable to explain the inclusion of nouns that share identical morphological and phonological features but different semantic features. Thus, this problem questions the assumption that noun classification in Kusaal is dependent on common semantic properties or coherence shared by all nouns in a group. The semantic classification of nouns, in this study, is composed based on the assumption that speakers of Kusaal put together nouns that are connected by identical semantic features and others that are linked by pragmatic associations into networks that define concepts and aspects of their survival as human beings. It is further observed that nouns within such groups also go through identical phonological rules or constraints. Nouns in this paper are classified based on their morphological features which are closely knit to their semantic networks as well as phonological constraints. The framework of Lexical-Phonology is employed in analyzing the morphophonological components of the nominal classification system of the language
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Sagna,, Serge. "Physical properties and culture-specific factors as principles of semantic categorisation of the Gújjolaay Eegimaa noun class system." Cognitive Linguistics 23, no. 1 (February 2012): 129–63. http://dx.doi.org/10.1515/cog-2012-0005.

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AbstractThis paper investigates the semantic bases of class membership in the noun class system of Gújjolaay Eegimaa (Eegimaa henceforth), a Niger-Congo and Atlantic language of the BAK group spoken in Southern Senegal. The question of whether semantic principles underlie the overt classification of nouns in Niger-Congo languages is a controversial one. There is a common perception of Niger-Congo noun class systems as being mainly semantically arbitrary. The goal of the present paper is to show that physical properties and culture-specific factors are central principles of semantic categorisation in the Eegimaa noun class system. I argue that the Eegimaa overt grammatical classification of nouns into classes is a semantic categorisation system whereby categories are structured according to prototypicality, family resemblance, metaphorical and metonymic extensions and chaining processes, as argued within the framework of Cognitive Linguistics. I show that the categorisation of entities in the Eegimaa nominal classification system productively makes use of physical properties such as shape as well as using culture-specific, less productive parameters for the semantic categorisation of entities denoted by nouns. The analysis proposed here also shows that the cases of multiple morphosyntactic classifications of nouns reflect multiple conceptual categorisation strategies. A detailed examination of the formal and semantic instances of multiple classification reveals the existence of conceptual correlations between the physical properties and the culture-specific semantic parameters of categorisation used in the Eegimaa noun class system.
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Joon-Kyung Cha. "Semantic Classification of Korean Abstract Nouns." Discourse and Cognition 16, no. 2 (August 2009): 149–68. http://dx.doi.org/10.15718/discog.2009.16.2.149.

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Shubina, E. L. "Problem of the Classification of Quantitative Noun in the German Language." MGIMO Review of International Relations, no. 1(40) (February 28, 2015): 237–43. http://dx.doi.org/10.24833/2071-8160-2015-1-40-237-243.

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This work is dedicated to the semantic classification of quantitative noun on the basis of a structural study (Nquant + (Adj) +N) (ein Glas frisches Wasser), since this model reveals the greatest variety of grammatical formulation. These word combinations can form by the genitive government eine Tasse starken Kaffees by the grammatical agreement ein Eimer kaltes Wasser, or by the adjunction mit einem Korb reife Apfel. The suggested classification of the noun performing the function of the first components is based on the form of the noun acting as the first component. Types of the first components fall into three groups: 1. The nouns, which specify quantitative characteristics of objects and substances. Two subgroups are also distingshed: word combinations with a noun in a singular form Nquant1a as the second component and word combinations with a noun in a plural form as the second component Nquant1b; 2. The nouns defining a group of living beings and objects Nquant2; 3. The nouns which formation is grounded on quantitative nouns Nquant3. Normative recommendations on the choice of subordinate connection type should be connected at least at the present stage of existence of German literary language, exactly with the semantics of the nouns which are the first components in these word combinations. The article illustrates that all types of constructions (organizes whether on the basis of government, agreement and or adjunction) are connected with the completely specific semantic characteristics of the name, i.e., these nouns belong to one of three groups of noun - first components.
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Sevdiyevna, Nuritdinova Rano. "Thematic Classification Of Onomastic Terms." American Journal of Social Science and Education Innovations 02, no. 11 (November 28, 2020): 193–200. http://dx.doi.org/10.37547/tajssei/volume02issue11-35.

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In linguistics, there are different ways of classifying the lexical richness of a particular language. In particular, classifications such as thematic groups of words, lexical-semantic groups of words, stylistic layers of lexicon, historical-etymological layers of words are widely used. However, in linguistics, it has become a tradition to first divide the nouns in the language into two major groups: common nouns and proper nouns. This classification is limited in size as it refers only to the noun family. In fact, all words (adjectives, rhymes, numbers, verbs, adverbs, adverbs) that do not fall into the category of nouns in the language can be placed as opposites. However, if we approach the issue from this point of view, the lexicon of the language can be divided into two large groups: a group of common (ordinary) nouns and a group of proper nouns. In onomastics, there is also the term appellate, appellate lexicon. The term does not refer to all common words in a language, but to words that are the basis or division of a noun. This means that the appellate lexicon is a branch of cognate words in the language that has the property of transitioning to the function of a pronoun.
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Phu, Vo Ngoc, Vo Thi Ngoc Tran, Vo Thi Ngoc Chau, Dat Nguyen Duy, and Khanh Ly Doan Duy. "Semantic lexicons of English nouns for classification." Evolving Systems 10, no. 3 (June 12, 2017): 501–65. http://dx.doi.org/10.1007/s12530-017-9188-6.

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Lammert, Marie. "Lexical plurals through meronymy and hyperonymy." Lexical plurals and beyond 39, no. 2 (December 31, 2016): 335–54. http://dx.doi.org/10.1075/li.39.2.07lam.

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This study uses meronymy and hyperonymy as semantic criteria applied to French collective nouns (CollNs) and lexical plural nouns (LPNs) in the issue of noun classification. After having outlined the semantic properties of CollNs and explained their links with meronymy and hyperonymy, LPNs are tested in different glosses related to these two relationships. These tests outline that CollNs and LPNs form two kinds of nouns that could hardly converge. They also enlighten the way the different subclasses of LPNs match with meronymy and hyperonymy.
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Stolbovskaya, Margarita Anatol'evna. "STRUCTURAL-SEMANTIC CLASSIFICATION OF COMPOUND NOUNS OF AVIATION ENGLISH." Philological Sciences. Issues of Theory and Practice, no. 4-2 (April 2018): 395–99. http://dx.doi.org/10.30853/filnauki.2018-4-2.41.

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CUCCHIARELLI, ALESSANDRO, DANILO LUZI, and PAOLA VELARDI. "Semantic tagging of unknown proper nouns." Natural Language Engineering 5, no. 2 (June 1999): 171–85. http://dx.doi.org/10.1017/s135132499900220x.

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In this paper, we describe a context-based method to semantically tag unknown proper nouns (U-PNs) in corpora. Like many others, our system relies on a gazetteer and a set of context-dependent heuristics to classify proper nouns. However, proper nouns are an open-end class: when parsing new fragments of a corpus, even in the same language domain, we can expect that several proper nouns cannot be semantically tagged. The algorithm that we propose assigns to an unknown PN an entity type based on the analysis of syntactically and semantically similar contexts already seen in the application corpus. The performance of the algorithm is evaluated not only in terms of precision, following the tradition of MUC conferences, but also in terms of information gain, an information theoretic measure that takes into account the complexity of the classification task.
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NULTY, PAUL, and FINTAN COSTELLO. "General and specific paraphrases of semantic relations between nouns." Natural Language Engineering 19, no. 3 (May 20, 2013): 357–84. http://dx.doi.org/10.1017/s1351324913000089.

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AbstractMany English noun pairs suggest an almost limitless array of semantic interpretation. A fruit bowl might be described as a bowl for fruit, a bowl that contains fruit, a bowl for holding fruit, or even (perhaps in a modern sculpture class), a bowl made out of fruit. These interpretations vary in syntax, semantic denotation, plausibility, and level of semantic detail. For example, a headache pill is usually a pill for preventing headaches, but might, perhaps in the context of a list of side effects, be a pill that can cause headaches (Levi, J. N. 1978. The Syntax and Semantics of Complex Nominals. New York: Academic Press.). In addition to lexical ambiguity, both relational ambiguity and relational vagueness make automatic semantic interpretation of these combinations difficult. While humans parse these possibilities with ease, computational systems are only recently gaining the ability to deal with the complexity of lexical expressions of semantic relations. In this paper, we describe techniques for paraphrasing the semantic relations that can hold between nouns in a noun compound, using a semi-supervised probabilistic method to rank candidate paraphrases of semantic relations, and describing a new method for selecting plausible relational paraphrases at arbitrary levels of semantic specification. These methods are motivated by the observation that existing semantic relation classification schemes often exhibit a highly skewed class distribution, and that lexical paraphrases of semantic relations vary widely in semantic precision.
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Dissertations / Theses on the topic "Semantic classification of nouns"

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DINIZ, PAULA SANTOS. "THE SEMANTIC CLASSIFICATION OF TECHNICAL COMPOUND NOUNS AND THEIR TRANSLATION TO PORTUGUESE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30060@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Este trabalho propõe uma classificação semântica dos compostos nominais técnicos em língua inglesa e a análise sintática e semântica das traduções para o português. Para tanto, faz-se um panorama da literatura sobre as relações semânticas dos compostos nominais em língua inglesa. A tipologia aqui proposta é, portanto, baseada em estudos clássicos sobre a semântica dos compostos nominais (Levi, 1978; Warren, 1978) e em pesquisas mais recentes — inseridas no escopo da Linguística Computacional e ou influenciadas pela Teoria do Léxico Gerativo, de Pustejovsky (1995) —, e adaptada para a natureza dos compostos nominais selecionados. A presente dissertação também analisa as traduções dos compostos nominais técnicos para o português, bem como a função das preposições nas estruturas com sintagmas preposicionados. O corpus foi retirado de um livro técnico da área de engenharia elétrica/eletrônica traduzido pela autora. Além da classificação semântica dos compostos nominais técnicos, propõe-se a criação de ontologias que contemplem os compostos com os mesmos núcleos ou modificadores, de modo a observar se núcleos ou modificadores iguais implicam a mesma categorização, e se é respeitada a relação de hiperonímia e hiponímia entre os compostos nominais inseridos na mesma ontologia.
The major purpose of this thesis is to suggest a semantic categorization of English technical noun compounds, as well as to analyze the semantics and syntax of the Portuguese renderings. First, the literature on semantic relations in English compound nouns is reviewed. The classification here suggested is therefore based on classic studies on the semantics of compound nouns (Levi, 1978; Warren, 1978) and on more recent research within the scope of Computational Linguistics, which are influenced by the Generative Lexicon Theory (Pustejovsky, 1995). The semantic categorization is also adapted to the data collected in this work. This thesis also analyzes the Portuguese translation of the English compound nouns, as well as the role of the prepositions in prepositional phrases. The data was taken from an electrical/electronics engineering book which was translated by the author. In addition to the semantic classification, the technical compound nouns are grouped together according to the head or modifiers of the structure, and assembled into ontologies. Compound nouns sharing a common head or modifier are grouped together, so as to investigate if there is a hypernym-hyponym relation among the compounds assembled in the same ontology.
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Watson, Rachel. "Kujireray : morphosyntax, noun classification and verbal nouns." Thesis, SOAS, University of London, 2015. http://eprints.soas.ac.uk/22829/.

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The thesis constitutes a first description of the Joola language Kujireray. In addition to a grammatical sketch, it comprises an analysis of the noun classification system in Kujireray, including a detailed treatment of verbal nouns and their interaction with this system. The analysis takes place within a Cognitive Linguistics framework. The noun classification system is shown to be semantically motivated along such parameters as number and physical configuration. The semantic analysis is carried out at the level of the noun class paradigm, which approach is able to draw a more fine-grained picture of the structure/organization of the system. However, it is recognized that noun classification operates on three distinct but interdependent levels - the paradigm, the noun class prefix, and the agreement pattern - all of which contribute meaning. The analysis also encompasses a detailed treatment of verbal nouns, as they interact within the noun classification system. It is shown that the formation of verbal nouns in various noun class prefixes is semantically motivated just as in the nominal domain, and furthermore that analogies can be drawn between the semantic domains in the nominal domain and the verbal one. The analysis is situated within a Cognitive Linguistics framework, whereby notions of embodied experience, encyclopaedic knowledge and metaphorical thought are invoked to account for the semantic organization of noun classification system. It is shown that noun formation in Kujireray is constructional, with individual components possessing underspecified semantics which are elaborated in combination with each other. Furthermore, it is the property of underspecification which accounts for the parallels between the nominal and verbal domains.
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Kauṇḍabhaṭṭa, Deshpande Madhav. "The meaning of nouns : semantic theory in classical and medieval India /." Dordrecht ; Boston ; London : Kluwer academic publishers, 1992. http://catalogue.bnf.fr/ark:/12148/cb37062128q.

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Cobbinah, Alexander Yao. "Nominal classification and verbal nouns in Baïnounk Gubëeher." Thesis, SOAS, University of London, 2013. http://eprints.soas.ac.uk/17370/.

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Sudre, Gustavo. "Characterizing the Spatiotemporal Neural Representation of Concrete Nouns Across Paradigms." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/315.

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Most of the work investigating the representation of concrete nouns in the brain has focused on the locations that code the information. We present a model to study the contributions of perceptual and semantic features to the neural code representing concepts over time and space. The model is evaluated using magnetoencephalography data from different paradigms and not only corroborates previous findings regarding a distributed code, but provides further details about how the encoding of different subcomponents varies in the space-time spectrum. The model also successfully generalizes to novel concepts that it has never seen during training, which argues for the combination of specific properties in forming the meaning of concrete nouns in the brain. The results across paradigms are in agreement when the main differences among the experiments (namely, the number of repetitions of the stimulus, the task the subjects performed, and the type of stimulus provided) were taken into consideration. More specifically, these results suggest that features specific to the physical properties of the stimuli, such as word length and right-diagonalness, are encoded in posterior regions of the brain in the first hundreds of milliseconds after stimulus onset. Then, properties inherent to the nouns, such as is it alive? and can you pick it up?, are represented in the signal starting at about 250 ms, focusing on more anterior parts of the cortex. The code for these different features was found to be distributed over time and space, and it was common for several regions to simultaneously code for a particular property. Moreover, most anterior regions were found to code for multiple features, and a complex temporal profile could be observed for the majority of properties. For example, some features inherent to the nouns were encoded earlier than others, and the extent of time in which these properties could be decoded varied greatly among them. These findings complement much of the work previously described in the literature, and offer new insights about the temporal aspects of the neural encoding of concrete nouns. This model provides a spatiotemporal signature of the representation of objects in the brain. Paired with data from carefully-designed paradigms, the model is an important tool with which to analyze the commonalities of the neural code across stimulus modalities and tasks performed by the subjects.
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Michalkova, Marcela. "Gender Asymmetries in Slovak Personal Nouns." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262189760.

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Hartung, Matthias [Verfasser], and Anette [Akademischer Betreuer] Frank. "Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns / Matthias Hartung ; Betreuer: Anette Frank." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180609360/34.

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Fallgren, Per. "Thoughts don't have Colour, do they? : Finding Semantic Categories of Nouns and Adjectives in Text Through Automatic Language Processing." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138641.

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Not all combinations of nouns and adjectives are possible and some are clearly more fre- quent than other. With this in mind this study aims to construct semantic representations of the two types of parts-of-speech, based on how they occur with each other. By inves- tigating these ideas via automatic natural language processing paradigms the study aims to find evidence for a semantic mutuality between nouns and adjectives, this notion sug- gests that the semantics of a noun can be captured by its corresponding adjectives, and vice versa. Furthermore, a set of proposed categories of adjectives and nouns, based on the ideas of Gärdenfors (2014), is presented that hypothetically are to fall in line with the produced representations. Four evaluation methods were used to analyze the result rang- ing from subjective discussion of nearest neighbours in vector space to accuracy generated from manual annotation. The result provided some evidence for the hypothesis which suggests that further research is of value.
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Romeo, Lauren Michele. "The Structure of the lexicon in the task of the automatic acquisition of lexical information." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/325420.

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La información de clase semántica de los nombres es fundamental para una amplia variedad de tareas del procesamiento del lenguaje natural (PLN), como la traducción automática, la discriminación de referentes en tareas como la detección y el seguimiento de eventos, la búsqueda de respuestas, el reconocimiento y la clasificación de nombres de entidades, la construcción y ampliación automática de ontologías, la inferencia textual, etc. Una aproximación para resolver la construcción y el mantenimiento de los léxicos de gran cobertura que alimentan los sistemas de PNL, una tarea muy costosa y lenta, es la adquisición automática de información léxica, que consiste en la inducción de una clase semántica relacionada con una palabra en concreto a partir de datos de su distribución obtenidos de un corpus. Precisamente, por esta razón, se espera que la investigación actual sobre los métodos para la producción automática de léxicos de alta calidad, con gran cantidad de información y con anotación de clase como el trabajo que aquí presentamos, tenga un gran impacto en el rendimiento de la mayoría de las aplicaciones de PNL. En esta tesis, tratamos la adquisición automática de información léxica como un problema de clasificación. Con este propósito, adoptamos métodos de aprendizaje automático para generar un modelo que represente los datos de distribución vectorial que, basados en ejemplos conocidos, permitan hacer predicciones de otras palabras desconocidas. Las principales preguntas de investigación que planteamos en esta tesis son: (i) si los datos de corpus proporcionan suficiente información para construir representaciones de palabras de forma eficiente y que resulten en decisiones de clasificación precisas y sólidas, y (ii) si la adquisición automática puede gestionar, también, los nombres polisémicos. Para hacer frente a estos problemas, realizamos una serie de validaciones empíricas sobre nombres en inglés. Nuestros resultados confirman que la información obtenida a partir de la distribución de los datos de corpus es suficiente para adquirir automáticamente clases semánticas, como lo demuestra un valor-F global promedio de 0,80 aproximadamente utilizando varios modelos de recuento de contextos y en datos de corpus de distintos tamaños. No obstante, tanto el estado de la cuestión como los experimentos que realizamos destacaron una serie de retos para este tipo de modelos, que son reducir la escasez de datos del vector y dar cuenta de la polisemia nominal en las representaciones distribucionales de las palabras. En este contexto, los modelos de word embedding (WE) mantienen la “semántica” subyacente en las ocurrencias de un nombre en los datos de corpus asignándole un vector. Con esta elección, hemos sido capaces de superar el problema de la escasez de datos, como lo demuestra un valor-F general promedio de 0,91 para las clases semánticas de nombres de sentido único, a través de una combinación de la reducción de la dimensionalidad y de números reales. Además, las representaciones de WE obtuvieron un rendimiento superior en la gestión de las ocurrencias asimétricas de cada sentido de los nombres de tipo complejo polisémicos regulares en datos de corpus. Como resultado, hemos podido clasificar directamente esos nombres en su propia clase semántica con un valor-F global promedio de 0,85. La principal aportación de esta tesis consiste en una validación empírica de diferentes representaciones de distribución utilizadas para la clasificación semántica de nombres junto con una posterior expansión del trabajo anterior, lo que se traduce en recursos léxicos y conjuntos de datos innovadores que están disponibles de forma gratuita para su descarga y uso.
La información de clase semántica de los nombres es fundamental para una amplia variedad de tareas del procesamiento del lenguaje natural (PLN), como la traducción automática, la discriminación de referentes en tareas como la detección y el seguimiento de eventos, la búsqueda de respuestas, el reconocimiento y la clasificación de nombres de entidades, la construcción y ampliación automática de ontologías, la inferencia textual, etc. Una aproximación para resolver la construcción y el mantenimiento de los léxicos de gran cobertura que alimentan los sistemas de PNL, una tarea muy costosa y lenta, es la adquisición automática de información léxica, que consiste en la inducción de una clase semántica relacionada con una palabra en concreto a partir de datos de su distribución obtenidos de un corpus. Precisamente, por esta razón, se espera que la investigación actual sobre los métodos para la producción automática de léxicos de alta calidad, con gran cantidad de información y con anotación de clase como el trabajo que aquí presentamos, tenga un gran impacto en el rendimiento de la mayoría de las aplicaciones de PNL. En esta tesis, tratamos la adquisición automática de información léxica como un problema de clasificación. Con este propósito, adoptamos métodos de aprendizaje automático para generar un modelo que represente los datos de distribución vectorial que, basados en ejemplos conocidos, permitan hacer predicciones de otras palabras desconocidas. Las principales preguntas de investigación que planteamos en esta tesis son: (i) si los datos de corpus proporcionan suficiente información para construir representaciones de palabras de forma eficiente y que resulten en decisiones de clasificación precisas y sólidas, y (ii) si la adquisición automática puede gestionar, también, los nombres polisémicos. Para hacer frente a estos problemas, realizamos una serie de validaciones empíricas sobre nombres en inglés. Nuestros resultados confirman que la información obtenida a partir de la distribución de los datos de corpus es suficiente para adquirir automáticamente clases semánticas, como lo demuestra un valor-F global promedio de 0,80 aproximadamente utilizando varios modelos de recuento de contextos y en datos de corpus de distintos tamaños. No obstante, tanto el estado de la cuestión como los experimentos que realizamos destacaron una serie de retos para este tipo de modelos, que son reducir la escasez de datos del vector y dar cuenta de la polisemia nominal en las representaciones distribucionales de las palabras. En este contexto, los modelos de word embedding (WE) mantienen la “semántica” subyacente en las ocurrencias de un nombre en los datos de corpus asignándole un vector. Con esta elección, hemos sido capaces de superar el problema de la escasez de datos, como lo demuestra un valor-F general promedio de 0,91 para las clases semánticas de nombres de sentido único, a través de una combinación de la reducción de la dimensionalidad y de números reales. Además, las representaciones de WE obtuvieron un rendimiento superior en la gestión de las ocurrencias asimétricas de cada sentido de los nombres de tipo complejo polisémicos regulares en datos de corpus. Como resultado, hemos podido clasificar directamente esos nombres en su propia clase semántica con un valor-F global promedio de 0,85. La principal aportación de esta tesis consiste en una validación empírica de diferentes representaciones de distribución utilizadas para la clasificación semántica de nombres junto con una posterior expansión del trabajo anterior, lo que se traduce en recursos léxicos y conjuntos de datos innovadores que están disponibles de forma gratuita para su descarga y uso.
Lexical semantic class information for nouns is critical for a broad variety of Natural Language Processing (NLP) tasks including, but not limited to, machine translation, discrimination of referents in tasks such as event detection and tracking, question answering, named entity recognition and classification, automatic construction and extension of ontologies, textual inference, etc. One approach to solve the costly and time-consuming manual construction and maintenance of large-coverage lexica to feed NLP systems is the Automatic Acquisition of Lexical Information, which involves the induction of a semantic class related to a particular word from distributional data gathered within a corpus. This is precisely why current research on methods for the automatic production of high- quality information-rich class-annotated lexica, such as the work presented here, is expected to have a high impact on the performance of most NLP applications. In this thesis, we address the automatic acquisition of lexical information as a classification problem. For this reason, we adopt machine learning methods to generate a model representing vectorial distributional data which, grounded on known examples, allows for the predictions of other unknown words. The main research questions we investigate in this thesis are: (i) whether corpus data provides sufficient distributional information to build efficient word representations that result in accurate and robust classification decisions and (ii) whether automatic acquisition can handle also polysemous nouns. To tackle these problems, we conducted a number of empirical validations on English nouns. Our results confirmed that the distributional information obtained from corpus data is indeed sufficient to automatically acquire lexical semantic classes, demonstrated by an average overall F1-Score of almost 0.80 using diverse count-context models and on different sized corpus data. Nonetheless, both the State of the Art and the experiments we conducted highlighted a number of challenges of this type of model such as reducing vector sparsity and accounting for nominal polysemy in distributional word representations. In this context, Word Embeddings (WE) models maintain the “semantics” underlying the occurrences of a noun in corpus data by mapping it to a feature vector. With this choice, we were able to overcome the sparse data problem, demonstrated by an average overall F1-Score of 0.91 for single-sense lexical semantic noun classes, through a combination of reduced dimensionality and “real” numbers. In addition, the WE representations obtained a higher performance in handling the asymmetrical occurrences of each sense of regular polysemous complex-type nouns in corpus data. As a result, we were able to directly classify such nouns into their own lexical-semantic class with an average overall F1-Score of 0.85. The main contribution of this dissertation consists of an empirical validation of different distributional representations used for nominal lexical semantic classification along with a subsequent expansion of previous work, which results in novel lexical resources and data sets that have been made freely available for download and use.
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Koivisto-Alanko, Päivi. "Abstract words in abstract worlds : directionality and prototypical structure in the semantic change in English nouns of cognition /." Helsinki : Société néophilologique, 2000. http://catalogue.bnf.fr/ark:/12148/cb392874530.

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Books on the topic "Semantic classification of nouns"

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Synonymy and semantic classification. Edinburgh: Edinburgh University Press, 1986.

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Sappan, Raphael. The rhetorical-logical classification of semantic changes. Braunton: Merlin, 1987.

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Emotive signs in language and semantic functioning of derived nouns in Russian. Amsterdam: J. Benjamins, 1987.

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Madhav, Deshpande, ed. The meaning of nouns: Semantic theory in classical and medieval India : Nāmārtha-nirṇaya of Kauṇḍabhaṭṭa. New Delhi: D.K. Printworld, 2007.

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Kauṇḍabhaṭṭa. The meaning of nouns: Semantic theory in classical and medieval India = Nāmārtha-nirṇaya of Kauṇḍabhaṭṭa. Dordrecht: Kluwer Academic Publishers, 1992.

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A classification of semantic case-relations in the Pauline Epistles. New York: P. Lang, 1997.

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Rundblad, Gabriella. Shallow brooks and rivers wide: A study of lexical and semantic change in English nouns denoting 'watercourse'. Stockholm, Sweden: Almqvist & Wiksell, 1998.

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Oppentocht, Anna Linnea. Lexical semantic classification of Dutch verbs: Towards constructing NLP and human-friendly definitions. Utrecht: LEd, 1999.

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Koivisto-Alanko, Päivi. abstract words Abstract words in abstract worlds: Directionality and prototypical structure in the semantic change in English nouns of cognition. Helsinki: Société Néophilologique, 2000.

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Laffling, John. Machine disambiguation and translation of polysemous nouns: A lexicon-driven model for text-semantic analysis and parallel text-dependent transfer in German-English translation of party political texts. Wolverhampton: Wolverhampton Polytechnic, School of Languages and European Studies, 1990.

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Book chapters on the topic "Semantic classification of nouns"

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Dakin, Karen. "Animals and vegetables, Uto-Aztecan noun derivation, semantic classification, and cultural history." In Historical Linguistics 1999, 105–17. Amsterdam: John Benjamins Publishing Company, 2001. http://dx.doi.org/10.1075/cilt.215.09dak.

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Seifart, Frank. "The semantic reduction of the noun universe and the diachrony of nominal classification." In Current Issues in Linguistic Theory, 9–32. Amsterdam: John Benjamins Publishing Company, 2018. http://dx.doi.org/10.1075/cilt.342.02sei.

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Rakhilina, Ekaterina V. "Aspectual classification of nouns." In Studies in Language Companion Series, 341. Amsterdam: John Benjamins Publishing Company, 1999. http://dx.doi.org/10.1075/slcs.50.23rak.

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Wu, Kejia, and Volker Haarslev. "Parallel OWL Reasoning: Merge Classification." In Semantic Technology, 211–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14122-0_17.

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Wu, Kejia, and Volker Haarslev. "Parallel OWL Reasoning: Merge Classification." In Semantic Technology, 211–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06826-8_17.

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Wang, Shan, and Chu-Ren Huang. "The semantic type system of event nouns." In Studies in Chinese Language and Discourse, 205–22. Amsterdam: John Benjamins Publishing Company, 2013. http://dx.doi.org/10.1075/scld.2.10wan.

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Barzegar, Siamak, Andre Freitas, Siegfried Handschuh, and Brian Davis. "Composite Semantic Relation Classification." In Natural Language Processing and Information Systems, 406–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59569-6_49.

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Piatrik, Tomas, and Ebroul Izquierdo. "Image Classification Using an Ant Colony Optimization Approach." In Semantic Multimedia, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930334_13.

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Dashdorj, Zolzaya, Muhammad Tahir Khan, Loris Bozzato, and SangKeun Lee. "Classification of News by Topic Using Location Data." In Semantic Technology, 305–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50112-3_23.

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Koivisto-Alanko, Päivi. "Mechanisms of Semantic Change in Nouns of Cognition." In Lexicology, Semantics and Lexicography, 35. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/cilt.194.06koi.

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Conference papers on the topic "Semantic classification of nouns"

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Chen, Keh-Jiann, and Chao-jan Chen. "Automatic semantic classification for Chinese unknown compound nouns." In the 18th conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/990820.990846.

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Kanzaki, Kyoko, Qing Ma, Masaki Murata, and Hitoshi Isahara. "Classification of adjectival and non-adjectival nouns based on their semantic behavior by using a self-organizing semantic map." In COLING-02. Morristown, NJ, USA: Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1118735.1118742.

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Moldovan, Dan, Adriana Badulescu, Marta Tatu, Daniel Antohe, and Roxana Girju. "Models for the semantic classification of noun phrases." In the HLT-NAACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1596431.1596440.

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Siu, Amy, and Gerhard Weikum. "Semantic Type Classification of Common Words in Biomedical Noun Phrases." In Proceedings of BioNLP 15. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-3811.

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Nulty, Paul. "Semantic classification of noun phrases using web counts and learning algorithms." In the 45th Annual Meeting of the ACL: Student Research Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1557835.1557853.

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Girju, Roxana, Ana-Maria Giuglea, Marian Olteanu, Ovidiu Fortu, Orest Bolohan, and Dan Moldovan. "Support vector machines applied to the classification of semantic relations in nominalized noun phrases." In the HLT-NAACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1596431.1596441.

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Lirong Qiu, Xiaobing Zhao, Jie Yuan, and Guosheng yang. "Measuring semantic nouns in Tibetan language." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5972041.

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Curran, James R. "Supersense tagging of unknown nouns using semantic similarity." In the 43rd Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1219840.1219844.

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Qian, Ting, Benjamin Van Durme, and Lenhart Schubert. "Building a semantic lexicon of English nouns via bootstrapping." In Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1620932.1620939.

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Ilina, Ekaterina G., and Ekaterina M. Vishnevskaya. "SEMANTIC ANALYSIS OF THE SYNONYMS PAIN / HURT / ACHE." In Люди речисты - 2021. Ulyanovsk State Pedagogical University named after I. N. Ulyanov, 2021. http://dx.doi.org/10.33065/978-5-907216-49-5-2021-41-46.

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The paper considers the semantic and structural features of synonymous nouns pain / hurt / ache from the perspective of cognitive linguistics. The study is based on the data from British and American English. The paper reveals the peculiarities of the semantic zones, where the investigated words are relevant and clarifies their definition. The study specifies the functioning of basic nouns that are used for pain description in the English language.
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Reports on the topic "Semantic classification of nouns"

1

Kasper, Robert T., and Eduard H. Hovy. Performing Integrated Syntactic and Semantic Parsing Using Classification. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada460334.

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Madani, Farshad. Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6116.

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Kud, A. A. Figures and Tables. Reprinted from “Comprehensive сlassification of virtual assets”, A. A. Kud, 2021, International Journal of Education and Science, 4(1), 52–75. KRPOCH, 2021. http://dx.doi.org/10.26697/reprint.ijes.2021.1.6.a.kud.

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Figure. Distributed Ledger Token Accounting System. Figure. Subjects of Social Relations Based on the Decentralized Information Platform. Figure. Derivativeness of a Digital Asset. Figure. Semantic Features of the Concept of a “Digital Asset” in Economic and Legal Aspects. Figure. Derivativeness of Polyassets and Monoassets. Figure. Types of Tokenized Assets Derived from Property. Figure. Visual Representation of the Methods of Financial and Management Accounting of Property Using Various Types of Tokenized Assets. Figure. Visual Representation of the Classification of Virtual Assets Based on the Complexity of Their Nature. Table. Comparison of Properties of Various Types of Virtual Assets of the Distributed Ledger Derivative of the Original Asset. Table. Main Properties and Parameters of Types of Tokenized Assets. Table. Classification of Virtual Assets as Tools for Implementing the Methods of Financial and Management Accounting of Property.
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Tabinskyy, Yaroslav. VISUAL CONCEPTS OF PHOTO IN THE MEDIA (ON THE EXAMPLE OF «UKRAINER» AND «REPORTERS»). Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11099.

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The article is devoted to the analysis of the main forms of visualization in the media related to photo. The thematic visual concepts are described in accordance with the content of electronic media, which consider the impact of modern technologies on the development of media space. The researches of the Ukrainian and foreign educational institutions concerning the main features of modern photo is classificate. Modifications and new visual forms in the media are singled out. The main objective of the article is to study the visual concepts of modern photo and identify ideological and thematic priorities in photo projects. To achieve the main objective in the article a certain methodology were used. Due to the historical-theoretical description it was possible to substantiate the study of visual concepts. The conceptual-system method was used to study the subject of media photo projects. The main results of the research are the definition of visual concepts of photo on the example of electronic media and the identification of the main thematic features in the process of visual filling of the media space. Based on the study, we can conclude that today the information field needs quality visual content. For successful creation of visual concepts it is necessary to single out thematic features of modern photo and to carry out classifications on ideological and semantic signs. Given the rapid development of digital technologies, the topic of the scientific article we offer is relevant for scientists, journalists, media researchers, visual journalism experts and photojournalists. Modern space is filled with a large number of pictorial materials, which in most cases form specific images, patterns or stereotypes in the mind of the reader (viewer). Also important is the classification of photo used in journalistic publications. That is why there is a need to explore the content and principles of distribution of ideological priorities of photo in the media. The substantiation of scientists about the important place of photography in the modern media space and the future development of visual technologies, which already use artificial intelligence, is relevant.
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