[PDF] Top 20 Lexical Substitution for Evaluating Compositional Distributional Models
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Lexical Substitution for Evaluating Compositional Distributional Models
... that lexical sub- stitution (McCarthy and Navigli, 2009) is a more suitable evaluation task for ...CDSMs. Lexical sub- stitution is the task of finding meaning-preserving substitutes for a target word in ... See full document
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Investigating the Contribution of Distributional Semantic Information for Dialogue Act Classification
... this distributional hypothe- sis have recently been applied to many tasks, but mostly at the word level: for instance, word sense disambiguation (Zhitomirsky-Geffet and Dagan, 2009) and lexical ... See full document
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A SICK cure for the evaluation of compositional distributional semantic models
... (similar meaning), the CONTRADICTION label in case of S1S3 pairs (contrast/contradiction), and the NEUTRAL la- bel in case of S1S4 pairs (lexical overlap only). We ob- serve however a relatively high proportion of ... See full document
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Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
... LSTM models in the context of a sen- timent classification task can be used to predict fMRI activations for positive versus negative af- fective ...a distributional approach, acting like a ‘graded ... See full document
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A critique of word similarity as a method for evaluating distributional semantic models
... Distributional models of lexical semantics have re- cently attracted considerable interest in the NLP community. With the increase in popularity, the issue of evaluation is becoming more important. ... See full document
6
General estimation and evaluation of compositional distributional semantic models
... extracted distributional semantic vectors us- ing as source corpus the concatenation of ukWaC, Wikipedia (2009 dump) and BNC, ...MEN lexical relatedness dataset, 8 we decided to use the top 10K most ... See full document
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Evaluating Distributional Models of Semantics for Syntactically Invariant Inference
... The idea of compositionality has been central to understanding contemporary natural language se- mantics from an historiographic perspective. The idea is often credited to Frege, although in fact Frege had very little to ... See full document
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Towards Syntax aware Compositional Distributional Semantic Models
... the lexical token similarity feature which is extremely relevant in ...baseline models in RTE and they approximate the corresponding STKs shows that it is possible to positively exploit structural ... See full document
10
Concrete Sentence Spaces for Compositional Distributional Models of Meaning
... Mitchell and Lapata introduce and evaluate a multiplicative model for vector composition [8]. The particular concrete construction of this paper differs from that of [8] in that our framework subsumes truth-theoretic as ... See full document
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Literal and Metaphorical Senses in Compositional Distributional Semantic Models
... Compositional distributional semantic models (CDSMs) provide a compact model of composi- tionality that produces vector representations of phrases while avoiding the sparsity and storage issues ... See full document
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MoL 2014 22: Categorical Foundations for Extended Compositional Distributional Models of Meaning
... in lexical semantics: how does one attribute a meaning to single words? A standard tool, initiated by Montague in the ’70s (Montague, 1970b), is to employ a set- theoretic lexical semantics, in which one ... See full document
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Polish evaluation dataset for compositional distributional semantics models
... Nowadays, distributional semantics models are estimated with various methods, ...its lexical context is associated with a distributional vector, the distance between distributional ... See full document
9
A Comparison of Context sensitive Models for Lexical Substitution
... The lexical substitution task consists in selecting meaning-preserving substitutes for words in ...of evaluating the in-context lexical inference capacity of vector-space models without ... See full document
12
Lexical Variability and Compositionality: Investigating Idiomaticity with Distributional Semantic Models
... the models employed in this first ...the models the suceeded the most in placing id- ioms before non-idioms in the obtained rankings and exhibited the best trade-off between precision and recall, as shown ... See full document
11
Typology of Adjectives Benchmark for Compositional Distributional Models
... of compositional distributional semantic models (CDSMs): prediction of lexical ...of distributional semantic ...for distributional models. We find that ... See full document
5
Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning
... discussed lexical DSMs and the problems faced by attempts to provide a vector composition operation that would allow us to form distributional sentence rep- resentations as a function of word ...categorical ... See full document
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Fusion of Compositional Network based and Lexical Function Distributional Semantic Models
... the distributional hypothesis of meaning, ...word lexical semantics, enabling the computation of word semantic similar- ity (Turney and Pantel, ...various compositional phenom- ena (Pelletier, 1994) ... See full document
9
Supervised All Words Lexical Substitution using Delexicalized Features
... supervised lexical substitu- tion system that does not use separate clas- sifiers per word and is therefore applicable to any word in the ...word-specific substitution patterns, a global model for ... See full document
11
Exploration of register dependent lexical semantics using word embeddings
... processing, distributional models, based on the foundational idea of ‘meaning as context’, are now one of the primary tools for semantic-related ...called distributional hypothesis, which states that ... See full document
9
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality
... space models from the neural network literature, which are usually motivated as a way of tackling data sparsity ...space models, their compositionality and how to use this new kind of representation in NLP ... See full document
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