[PDF] Top 20 Compositional Matrix Space Models of Language
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Compositional Matrix Space Models of Language
... Vector Space Models (Salton et ...Word Space Models (Schütze, 1993), Hyperspace Analogue to Lan- guage (Lund and Burgess, 1996), or Latent Se- mantic Analysis (Deerwester et ...Vector ... See full document
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On the Correspondence between Compositional Matrix Space Models of Language and Weighted Automata
... Compositional matrix-space models of language were recently proposed for the task of meaning representation of complex text structures in natural language process- ...These ... See full document
5
Gradual Learning of Matrix Space Models of Language for Sentiment Analysis
... Compositional Matrix-Space Models (CMSMs) consider compositionality in language by the fol- lowing general idea: the semantic space consists of quadratic matrices carrying real ... See full document
8
Compositional Matrix Space Models for Sentiment Analysis
... however, models just pos- itive ...register compositional effects in sentiment brought about by intensifiers like “very”, “absolutely”, “extremely”, ...natural language process- ing tasks including ... See full document
11
Multilingual Models for Compositional Distributed Semantics
... Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar ...these models on two ... See full document
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The Role of Syntax in Vector Space Models of Compositional Semantics
... the compositional process by which the meaning of an utterance arises from the meaning of its parts is a funda- mental task of Natural Language Process- ...vector space representations of sentential ... See full document
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Typology of Adjectives Benchmark for Compositional Distributional Models
... of compositional distributional models on this typological similarity ...capture language- independent ...single language were able to predict in an unsupervised setting typological patterns ... See full document
5
A Compositional and Interpretable Semantic Space
... Typically, word usage statistics used to create a VSM form a sparse matrix with many columns, too unwieldy to be practical. Thus, most models use some form of dimensionality reduction to compress the full ... See full document
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Investigating the Contribution of Distributional Semantic Information for Dialogue Act Classification
... to compositional vector space modelling have been successfully applied to capture the meaning of a phrase in a range of tasks (Mitchell and Lapata, 2008; Grefenstette and Sadrzadeh, 2011; Socher et ... See full document
8
A Compositional Bayesian Semantics for Natural Language
... Our semantics draws inspiration from (i) Montague semantics, (ii) vector space models, and (iii) Bayesian inference. Additionally, the implementation is guided by programming language theory. At the ... See full document
10
Compositional Vector Space Models for Knowledge Base Completion
... as matrix completion (likewise using vector embeddings), but predicts textually- defined OpenIE relations as well as KB relations, and embeds entity-pairs in addition to individual ... See full document
11
Training Continuous Space Language Models: Some Practical Issues
... the matrix operations and cut down both inference and training ...neural language models on very large cor- pora; it has also been observed empirically that sam- pling the training data can increase ... See full document
11
Lexical Substitution for Evaluating Compositional Distributional Models
... Compositional Distributional Semantic Mod- els (CDSMs) model the meaning of phrases and sentences in vector space. They have been predominantly evaluated on limited, ar- tificial tasks such as semantic ... See full document
6
Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models
... vector space semantic parsing (VSSP), a general framework for building compositional models of vector space ...ural language into logical forms, which are in turn evaluated to produce ... See full document
10
Broad coverage CCG Semantic Parsing with AMR
... We propose a grammar induction tech- nique for AMR semantic parsing. While previous grammar induction techniques were designed to re-learn a new parser for each target application, the recently anno- tated AMR Bank ... See full document
12
Estimating Linear Models for Compositional Distributional Semantics
... Yet, compositional dis- tributional models depend on a large set of parameters that have not been ...distributional models: the addi- tive ...extracting compositional distributional se- ... See full document
9
Investigating Continuous Space Language Models for Machine Translation Quality Estimation
... n-gram language model. The projection of the words onto the continuous space and the training of the neural network is done by the stan- dard back-propagation algorithm and outputs are the converged ... See full document
6
Compositional Lexical Semantics In Natural Language Inference
... the language component of natural language inference, so that one can reason about how natural language expressions relate to one ...natural language processing applications, such as Question ... See full document
199
Letter N Gram based Input Encoding for Continuous Space Language Models
... network language models uses a 1-of-n coding to insert a word from the vocabulary into the ...machine language model is proposed using such a softmax layer for each con- ... See full document
10
Storing the Web in Memory: Space Efficient Language Models with Constant Time Retrieval
... three models we present in this paper perform queries in O(1) time and are thus asymptotically optimal, but this does not guarantee they perform well in practice, therefore in this section we mea- sure query speed ... See full document
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