[PDF] Top 20 Multi Prototype Vector Space Models of Word Meaning
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Multi Prototype Vector Space Models of Word Meaning
... (top word), the multi-prototype pre- diction is chosen significantly more frequently ...gle prototype, our approach produces better near- synonyms for homonyms compared to ... See full document
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Modeling Word Meaning: Distributional Semantics and the Corpus Quality Quantity Trade Off
... require models to test directly on the brain activity associated with language, we believe they are a good approach to test models of the ...the vector of activations of a specific fMRI feature for ... See full document
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Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
... years, vector space models (VSMs) have been proved successful in solving various NLP tasks including named entity recognition, part-of-speech tagging, parsing, semantic role- labeling and answering ... See full document
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Looking at word meaning An interactive visualization of Semantic Vector Spaces for Dutch synsets
... a word. The type-level models are mostly used to retrieve semantic relations between words, ...Token-level models are typically used to distinguish between the different meanings within the uses of ... See full document
9
A comparison of models of word meaning in context
... the vector of the target acquire with the second order vector of the context knowledge, ...the vector of similarities of knowledge to all other words in the ...the vector of knowledge with the ... See full document
5
On Modeling Sense Relatedness in Multi prototype Word Embedding
... individual word is represented as multiple vectors, where each vector corresponds to one of its ...induce word senses (each clus- ter refers to one of the senses) and then learn the word sense ... See full document
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Word Space Models of Lexical Variation
... infrequent meaning that is typical of Bel- gian ...the word tenor is obviously the ‘male singer’ ...context vector fails to pick it up, and almost all nearest Netherlandic neighbours are re- lated to ... See full document
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Syntax Aware Multi Sense Word Embeddings for Deep Compositional Models of Meaning
... compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a pop- ular area of NLP ...of word embeddings ... See full document
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Vector space models for PPDB paraphrase ranking in context
... contextualised vector is derived from the ba- sic meaning vector of a target word w by reinforc- ing its dimensions that are licensed by the context of the specific instance under ...syntactic ... See full document
7
A Probabilistic Model for Learning Multi Prototype Word Embeddings
... based models to obtain distributed word representations (Morin and Bengio, 2005) (Mnih and Hinton, 2007) (Mikolov et ...these models assume that one word has only one embedding, except the ... See full document
10
Word Meaning in Context: A Simple and Effective Vector Model
... Distributional vector-space models of word mean- ing have proven helpful for a number of basic natu- ral language processing tasks, such as word sense discrimination (Schütze, 1998) and ... See full document
10
Ontologically Grounded Multi sense Representation Learning for Semantic Vector Space Models
... There are several reasons to prefer ontologies as distant sources of supervision for learning sense- aware VSMs over previously proposed unsuper- vised context clustering techniques. Clustering ap- proaches must often ... See full document
11
Exemplar Based Models for Word Meaning in Context
... a prototype-based model that ranks paraphrase can- didates by the distance between their type vectors and the target’s type ...plar models outperform this prototype ... See full document
6
Feature2Vec: Distributional semantic modelling of human property knowledge
... each word. Neural embedding models, on the other hand, learn from large corpora of text in an unsupervised fashion, allowing very detailed, high-dimensional semantic models to be constructed for a ... See full document
7
Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models
... present vector space semantic parsing (VSSP), a framework for learning compo- sitional models of vector space ...to vector space representa- ... See full document
10
Latent Vector Weighting for Word Meaning in Context
... our models are able to improve signifi- cantly over the dependency-based vector space base- ...our models generally score a bit lower compared to the English ... See full document
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Measuring Word Relatedness Using Heterogeneous Vector Space Models
... capture word senses using different vectors (Reisinger and Mooney, 2010) from the same text corpus, this is in fact difficult in ...one word sense. For example, in the Wikipeida term vector that ... 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
Detecting Compositionality of Multi Word Expressions using Nearest Neighbours in Vector Space Models
... and vector space models (Sch¨utze, 1993; Tur- ney and Pantel, 2010; Erk, ...compositional models (Widdows, 2008; Mitchell and Lapata, 2010; Baroni and Zamparelli, 2010; Co- ecke et ... See full document
6
What Is Word Meaning, Really? (And How Can Distributional Models Help Us Describe It?)
... distributional models are an obvious choice, if we can just represent each individual usage as a point in ...However, vector space models have mostly been used to represent the meaning ... See full document
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