2024-03-28T14:17:15Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/3742342024-03-15T10:58:13Zcom_10803_236col_10803_690280
nam a 5i 4500
Lexical-semantic relations
Semantics
Semantic relations
Natural Language Processing
Lexical-semantic information acquisition
Data sparsity
Word pair representations
Distributional hypothesis
Latent relational hypothesis
Hypernyms
Co-hyponyms
Meronyms
Selectional preferences
Graph theory
Word embeddings
Relacions lexicosemàntiques
Semàntica
Relacions semàntiques
Processament del Llenguatge Natural
Adquisicio d’informació lexicosemantica
Representaciò de parells de paraules
Hipòtesi distribucional
Hiperònims
Cohipònims
Merònims
Preferencies de selecció
Teoría dels grafs
Automatic acquisition of lexical-semantic relations: gathering information in a dense representation
[Barcelona] :
Universitat Pompeu Fabra,
2016
Accés lliure
http://hdl.handle.net/10803/374234
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Necşulescu, Silvia,
autor
Programa de doctorat en Traducció i Ciències del Llenguatge,
degree
1 recurs en línia (147 pàgines)
Tesi
Doctorat
Universitat Pompeu Fabra. Departament de Traducció i Ciències del llenguatge
2016
Universitat Pompeu Fabra. Departament de Traducció i Ciències del llenguatge
Tesis i dissertacions electròniques
Bel Rafecas, Núria,
supervisor acadèmic
TDX
Lexical-semantic relationships between words are key information for many NLP tasks, which require this knowledge in the form of lexical resources. This thesis addresses the acquisition of lexical-semantic relation instances. State of the art systems rely on word pair representations based on patterns of contexts where two related words co-occur to detect their relation. This approach is hindered by data sparsity: even when mining very large corpora, not every semantically related word pair co-occurs or not frequently enough.
In this work, we investigate novel representations to predict if two words hold a lexical-semantic relation. Our intuition was that these representations should contain information about word co-occurrences combined with information about the meaning of words involved in the relation. These two sources of information have to be the basis of a generalization strategy to be able to provide information even for words that do not co-occur.
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