[PDF] Top 20 From Frequency to Meaning: Vector Space Models of Semantics
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From Frequency to Meaning: Vector Space Models of Semantics
... Word similarity: Deerwester et al. (1990) discovered that we can measure word simi- larity by comparing row vectors in a term–document matrix. Landauer and Dumais (1997) evaluated this approach with 80 multiple-choice ... See full document
48
The Role of Syntax in Vector Space Models of Compositional Semantics
... treat semantics, and thus compositionality, essen- tially as an extension of syntax, with the generative (syntactic) process yielding a structure that can be interpreted ...and meaning comes at a price in ... See full document
11
Vector space models for PPDB paraphrase ranking in context
... using vector-space models of semantics which calculate the meaning of word occurrences in context based on distributional representations (Mitchell and La- pata, 2008; Erk and Pad´o, ... See full document
7
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality
... word vector space models, they are severely limited since they do not capture compositionality, the important quality of natural language that allows speakers to determine the meaning of a ... See full document
10
A Generative Model of Vector Space Semantics
... same vector space ...have from which to es- timate a distributional ...sitional vector space models of ...the meaning of a sentence is composed from the individual ... See full document
9
Multi Prototype Vector Space Models of Word Meaning
... multi-prototype vector space model for lexical semantics with a single parame- ter K (the number of clusters) that generalizes both prototype (K = 1) and exemplar (K = N , the total number of ... See full document
9
Exploration of register dependent lexical semantics using word embeddings
... distributional models, based on the foundational idea of ‘meaning as context’, are now one of the primary tools for semantic-related ...stem from the so called distributional hypothesis, which states ... See full document
9
Vector space semantics with frequency driven motifs
... derived from arbitrary ...the meaning of the token ‘crisis’ that a conven- tional DSM might extract from the given sentence after stopword ...crisis. From a semantic perspective, a ... See full document
10
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality
... word vector space models, they are severely limited since they do not capture compositionality, the important quality of natural language that allows speakers to determine the meaning of a ... See full document
10
Measuring Word Relatedness Using Heterogeneous Vector Space Models
... lar meaning (Harris, ...term- vector that consists of words occurring in its con- ...term vector: each term is weighted by log(f req) × log(N/df ), where f req is the number of times the term appears ... See full document
5
Vector Space Models for Scientific Document Summarization
... term- frequency (TF) vector space method utilizing a nonnegative matrix factorization (NNMF) for dimensionality ...derived from auxil- iary documents that cite the document of in- ... See full document
6
“Not not bad” is not “bad”: A distributional account of negation
... shifted from using distributional methods for modelling the semantics of words to using them for mod- elling the semantics of larger linguistic units such as phrases or entire ...move from ... See full document
9
Looking at word meaning An interactive visualization of Semantic Vector Spaces for Dutch synsets
... distributional models can capture word meaning to some extent, most of them use SVSs only in an indirect, black-box way, without an- alyzing which semantic properties and relations actually manifest ... See full document
9
A Structured Vector Space Model for Hidden Attribute Meaning in Adjective Noun Phrases
... Distributional semantics. We observe two re- cent trends in distributional semantics research: (i) The use of VSM tends to shift from mea- suring unfocused semantic similarity to captur- ing ... See full document
9
Applicative structure in vector space models
... that meaning of linguistic expressions can be for- malized using predicates with at most two argu- ments (there are no ditransitive or higher arity predicates on the semantic ... See full document
5
A Structured Vector Space Model for Word Meaning in Context
... previous models, and thus addresses the issues we have discussed in Section ...lexical vector of draw into the di- rection of pulling, while its inverse object preference (“things that are done to horses”) ... See full document
10
Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition
... word meaning (Murphy et ...Topic models (Blei et ...quent models added increasing linguistic sophisti- cation, up to full syntactic and dependency parses (Lin, 1998; Pad´o and Lapata, 2007; Baroni ... See full document
10
Vector Space Models for Phrase based Machine Translation
... formation about the represented words. Such em- beddings open the potential for applying informa- tion retrieval approaches where it becomes possi- ble to define and compute similarity between dif- ferent words. We focus ... See full document
10
Crosslingual and Multilingual Construction of Syntax Based Vector Space Models
... sentence. It has been applied to various linguistic levels such as POS tagging and syntax (Hi and Hwa, 2005; Hwa et al., 2005, among others). Other studies use parallel data as indirect supervision for monolin- gual ... See full document
14
Feature2Vec: Distributional semantic modelling of human property knowledge
... semantic models on their abil- ity to encode grounded, human-elicited seman- tic ...semantic models fail to predict attributive properties of concept words ...built from text corpora fail to capture ... See full document
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