[PDF] Top 20 Word Space Models of Lexical Variation
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Word Space Models of Lexical Variation
... key- word approaches are present in our Gold Standard, giving a precision of 10% and a recall of ...our word space model makes this figure rise to 29 correct markers, resulting in a precision of 29% ... See full document
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Automatically Linking Lexical Resources with Word Sense Embedding Models
... target word and a set of context words, it calculates the posterior probability of each sense of the target word given the context words according to the hierarchi- cal softmax model (Equation ...The ... See full document
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation
... specialisation models to emphasise lexical contrast in the fine-tuned vector space: ...boosting lexical con- trast. Our specialised word vector spaces yield state- of-the-art results on ... See full document
7
Specialising Word Vectors for Lexical Entailment
... Vector Space Specialisation A standard ap- proach to incorporating external information into vector spaces is to pull the representations of simi- lar words closer ...Some models integrate such constraints ... See full document
12
Multi Prototype Vector Space Models of Word Meaning
... vector- space word meaning that represents words as col- lections of prototype vectors, naturally accounting for lexical ...uses word sense discovery to partition a word’s con- texts and ... See full document
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Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases
... Each word was represented by a feature vector in word2vec spaces or their ...(W2V) models of blogs corpus with different space sizes (dimensionality=1-10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 150, ... See full document
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Measuring Word Relatedness Using Heterogeneous Vector Space Models
... entailment, word sense disambiguation, in- formation retrieval and automatic thesaurus discov- ...a lexical database such as WordNet (Budanitsky and Hirst, 2006; Agirre et ...vector space ... See full document
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Determining Compositionality of Word Expressions Using Word Space Models
... WSMs and their parameters WSMs can be built by different algorithms including LSA (Landauer and Dumais, 1997), Hyperspace Analogue to Lan- guage (HAL) (Lund and Burgess, 1996), Random Indexing (RI) (Sahlgren, 2005), and ... See full document
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Determining Compositionality of Expresssions Using Various Word Space Models and Methods
... Vector Space Model (VSM) and Hyperspace Ana- logue to Language ...vanced models include Latent Semantic Analy- sis (LSA), which is based on VSM, and Corre- lated Occurrence Analogue to Lexical ... See full document
10
Unsupervised Lexicon Discovery from Acoustic Input
... that models which jointly represent mul- tiple levels of linguistic structure often benefit from synergistic interactions (Johnson, 2008b) where dif- ferent levels of linguistic structure provide mutual ... See full document
16
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 ...each word is rep- resented as a vector of frequencies for this ... See full document
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A good space: Lexical predictors in word space evaluation
... Vector space models benefit from using an outside corpus to train the ...vector space based extraction ...of lexical measures commonly used in readability ...on lexical measures as ... See full document
6
Towards a Matrix based Distributional Model of Meaning
... A somewhat related task is the task of finding out to what extent (statistical) similarity measures cor- relate with free word associations 2 . Furthermore, this task was suggested as a shared task for the eval- ... See full document
6
What Is Word Meaning, Really? (And How Can Distributional Models Help Us Describe It?)
... in space, namely the vector for contract, and to trig- ger the inference rule for an occurrence of catch if it is close enough to the attachment ...a word sense not as a point but as a region in vector ... See full document
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A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
... sionally, the n-best list had no translation hy- potheses with diverse formality, so the FSMT sys- tem dropped necessary words, appended inessen- tial words, or selected improper or even incorrect words to fit the target ... See full document
6
The Word Frequency Effect: Relationship of Lexical Entries Between the Primary and Secondary Language
... for word frequency, there are a few other variables that may have influenced word recognition performance, like phonological irregularity, which is the inconsistency between the spelling to sound ... See full document
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Modeling Word Meaning: Distributional Semantics and the Corpus Quality Quantity Trade Off
... The Google Web corpus (Web) (Brants, 2006) contains n-grams of length up to 5 generated from publicly accessible Web pages. The Google Books dataset (Books) containing n-grams up to length 5 is extracted from a ... See full document
16
Discriminative Pronunciation Modeling: A Large Margin, Feature Rich Approach
... icantly. However, both baselines do quite poorly. Table 2 shows the best previous result on this data set from the articulatory model of Jyothi et al., which greatly improves over our baselines as well as over a much ... See full document
10
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
... 1). Models equipped with semantic features constantly ben- efit from such ...new word types, or expand the number of senses covered by the already existing classifiers (Fig ... See full document
6
One Lexicon, Two Structures: So What Gives?
... The question of a fast and easy access between lexical agregates remains. More detailed observa- tion would be required to determine why such an access is not possible in the LNs. The en-LN is made up mostly of ... See full document
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