word space
The Construction and Evaluation of Word Space Models
... Next, we investigated the performance of two Word Space Models. While parameters like context size or similarity metric were kept constant, they were constructed from very different data. One was based on a ...
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A good space: Lexical predictors in word space evaluation
... vector space used for representing the words and sentences is among other things dependent on what material that was used to create the vector ...vector space to a given task; being it text categorization, ...
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Word Space Models of Lexical Variation
... the word tenor is obviously the ‘male singer’ ...abandon word space models that build only one context vector per ...a word, with the aim of identifying context clusters that correspond to the ...
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Uncovering Distributional Differences between Synonyms and Antonyms in a Word Space Model
... all word classes are useful for modelling the con- textual differences between adjectival synonyms and antonyms, is also supported by the findings: the word space models built on the class of col- ...
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Embedding a Semantic Network in a Word Space
... linked word senses into a continuous-vector word space; the method is agnostic about whether the original word space was produced using a context-counting or context- predicting ...
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Enhancing extraction based summarization with outside word space
... The word space can be constructed from a ma- trix where text units are columns and the words in all text units are rows in the ...the word correspond- ing to the row exists in the text unit ...
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The S Space Package: An Open Source Package for Word Space Models
... for word space algo- rithms. While other word space frameworks ex- ist, ...for word spaces, such as tokenizing and filtering, sparse vectors and matrices, specialized data structures, ...
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Determining Compositionality of Expresssions Using Various Word Space Models and Methods
... WSM construction Since the DISCO and Reddy data were extracted from the ukWaC cor- pus (Baroni et al., 2009), we also build our WSMs from the same corpus. We use our own modifica- tion of the S-Space package ...
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Word Space Modeling for Measuring Semantic Specificity in Chinese
... acquisition. Data analyzed in this part include five groups of respondents’ usages of verbs to four different films, each of which pictures one event. Respondents are assigned into five groups according to their age: ...
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Determining Compositionality of Word Expressions Using Word Space Models
... We built LSA WSM from the whole ukWaC POS-tagged corpus for all the word lemmas con- catenated with their POS tags excluding stopwords. We treated the following strings as stopwords: the lemmas with frequency ...
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Automatic Word Sense Discrimination
... Similarity in Word Space is based on second-order co-occurrence: two tokens or contexts of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in tu[r] ...
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A Survey on Automatic Recognition of Speech via Voice Commands
... continuous word space vector, concepts consist of c1, c2, ...continuous word representation vector v(ci) videos are And this is achieved by using dot product similarity measure show in ...continuous ...
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Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
... The word similarity task is to calculate Spear- man’s correlation (Daniel, 1990) between two words as word vector ...extrofitted word with FrameNet is less than the other lexicons, its per- formance ...
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A Word Segmentation System for Handling Space Omission Problem in Urdu Script
... orthographic word represents a single word or a multiple word ...a word cluster can be segmented into words in multiple ways, the correct word boundary detection becomes a ...unknown ...
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Vector space calculation of semantic surprisal for predicting word pronunciation duration
... spoken word durations over and above more simple factors that are known to influence word durations, such as word length, frequency and predictability using a simpler language ...of word ...
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Word Embeddings as Metric Recovery in Semantic Spaces
... Continuous word representations have been remarkably useful across NLP tasks but re- main poorly ...ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these ...
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation
... A popular solution to address the limitation con- cerning lexical contrast is to move beyond stand- alone unsupervised learning. Post-processing pro- cedures have been designed that leverage exter- nal lexical knowledge ...
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Word Sense Disambiguation Based on Structured Semantic Space
... Word Sense Disambiguation Based on Structured Semantic Space*.. semantic space, as a foundation for word sense.[r] ...
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Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation
... standard word embed- dings encode more information than what is im- mediately obvious, yet their potential performance is limited by the impossibility of optimally sur- facing divergent linguistic information at ...
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What Is Word Meaning, Really? (And How Can Distributional Models Help Us Describe It?)
... in space. However, vector space models have mostly been used to represent the meaning of a word in isolation: The vector for a word is com- puted by summing over all its corpus occurrences, ...
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