[PDF] Top 20 A Study on Word Similarity using Context Vector Models
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A Study on Word Similarity using Context Vector Models
... We adopted two methods for measuring similarity; one used Equation TF × IDF, and the other used Equation TF. The results are shown in Figure 3 to Figure 6 in the Appendix. We measured the performance by computing ... See full document
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Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations
... of word vector space ...of vector space models not fully addressed to ...pilot study, state-of-the-art word embeddings perform reasonably well in the task but are still far from ... See full document
8
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 ...window-based context words and ... See full document
11
A Comparison of Context sensitive Models for Lexical Substitution
... substitutes, using the approach proposed by Peters et ...supervised word sense disambiguation ...task using contexts from the FC ...target word is the average of the ELMo vectors obtained from ... See full document
12
Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation
... of word vectors in a post-processing step, including neural embed- ding models that have superseded these traditional count-based models as we in fact do in this ...that word similarity ... See full document
10
Dual Embeddings and Metrics for Relational Similarity
... of word embeddings and different metrics. Word embeddings can capture topical and functional in- formation of a word by using different types of contexts, however they are unable to model the ... See full document
7
Redefining Context Windows for Word Embedding Models: An Experimental Study
... semantic models learn vec- tor representations of words through the contexts they occur ...of context (which often takes the form of a sliding window) has a direct in- fluence on the resulting embeddings, ... See full document
5
Measuring Word Relatedness Using Heterogeneous Vector Space Models
... entailment, word sense disambiguation, in- formation retrieval and automatic thesaurus discov- ...and similarity measures operating on a lexical database such as WordNet (Budanitsky and Hirst, 2006; Agirre ... See full document
5
Learning Text Pair Similarity with Context sensitive Autoencoders
... pairwise context-sensitive Autoencoder for computing text pair sim- ...into context-sensitive representations and uses them to compute similarity between text ...state-of-the-art models in two ... See full document
11
Probabilistic models of similarity in syntactic context
... Distributional models of lexical semantics, which assume that aspects of a word’s meaning can be re- lated to the contexts in which that word is typically used, have a long history in Natural Language Pro- ... See full document
11
Probabilistic Modeling of Joint context in Distributional Similarity
... robust context modeling scheme it is hard for dis- tributional similarity models to effectively lever- age larger word window contexts for measuring semantic ...feature vector ... 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
Efficient Non parametric Estimation of Multiple Embeddings per Word in Vector Space
... embeddings using the same corpus and vocabulary as used in Huang et al (2012), which is the April 2010 snap- shot of the Wikipedia corpus (Shaoul and West- bury, ...maximum context window (N ) of length 5 ... See full document
11
Improving sparse word similarity models with asymmetric measures
... to word similarity becomes clear when we consider that for many applications, word similarity measures need to be well-defined when comparing very fre- quent words with infrequent ...a ... See full document
6
Probabilistic Models of Cross Lingual Semantic Similarity in Context Based on Latent Cross Lingual Concepts Induced from Comparable Data
... a similarity function influences the ...standard similarity functions ...SF, context-aware models that modulate out-of- context word representations using the knowledge of ... See full document
14
Topic Models for Meaning Similarity in Context
... target word, we pool together all the substitutes given for all context ...a context as we extract only patterns containing tar- get words together with their X and Y ...The models assign ... See full document
9
Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces
... When we cluster test instances, we specify an upper limit on the number of clusters that can be discovered. In these experiments that value is 7. This reflects the fact that we do not know a–priori the number of possible ... See full document
8
A comparison of models of word meaning in context
... posed models for computing context sensitive word ...these models, simplify their formula- tion and evaluate them in a unified ...the models are essentially equivalent if syntactic ... See full document
5
Measuring Distributional Similarity in Context
... meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and para- phrases to word sense disambiguation and ... See full document
11
Exemplar Based Word Space Model for Compositionality Detection: Shared Task System Description
... Cosine similarity (sim) is used to mea- sure the similarity between two ...the vector representing con- text words are set to the ratio of the probability of the context word given the ... See full document
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