Word vector
Neural Vector Conceptualization for Word Vector Space Interpretation
... Some of the peaks are also reflected in the bot- tom plot of Fig. 1, which depicts the activation profile of the cosine similarity baseline method. The most notable difference between our method and the baseline is that ...
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Correlations between Word Vector Sets
... Cosine similarity between covariances is an in- stance of the RV coefficient and its uncentered version was applied in the context of word em- beddings before (Botev et al., 2017). We arrive at a similar ...
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Evaluation of Word Vector Representations by Subspace Alignment
... existing word similarity and text categoriza- tion evaluations yield different orderings of word vector ...evaluating word vector models solely on downstream tasks, and the need of a ...
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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 ...state-of-the-art word embeddings perform reasonably well in the task but are still far from human ...the word ...
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Correlation based Intrinsic Evaluation of Word Vector Representations
... limited to semantic evaluation, but are general approaches, that can evaluate word vector con- tent with respect to desired linguistic proper- ties. Semantic and syntactic linguistic features that we use to ...
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Document Level Machine Translation with Word Vector Models
... We have presented an evaluation of word vector models trained with neural networks. We test them in a document-level machine translation environ- ment. First, we build monolingual and bilingual models using ...
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Word Vector Representations to build an Emojifier using LSTM
... implements word vectors that allows us to implement a model which inputs a sentence and find the most appropriate emoji to be used with the ...uses word vector concept in Deep Learning domain of ...
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Rotated Word Vector Representations and their Interpretability
... rotated word vector can be used to understand what the word vectors are comprised ...a word can have multiple mean- ing as a polysemy, contain information of syntac- tic structure in its ...
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Non distributional Word Vector Representations
... task-independent word vector representations using linguistic knowledge derived from pre-constructed linguistic resources like WordNet (Miller, 1995), FrameNet (Baker et ...such word vectors every ...
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Traversal Free Word Vector Evaluation in Analogy Space
... for word analogy ques- tions (A to B is as C to D) in word vector ...fourth word by the given three, we measure the similar- ity directly on the “relations” of two pairs of given words, just ...
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Sparse Overcomplete Word Vector Representations
... in word vector di- mensions has been shown to improve dimension interpretability (Murphy et ...of word vectors as features in downstream tasks (Guo et al., 2014). The word vectors we produce ...
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Punctuation Prediction for Unsegmented Transcript Based on Word Vector
... use word vectors as the features in predicting punctuation marks for unsegmented transcript in a pure lexical ...pre-trained word vectors are fed into proposed models based on DNN or CNN for ...input ...
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Subword level Word Vector Representations for Korean
... Every word can be decomposed into a sequence of characters, which in turn can be de- composed into jamos, the smallest lexicographic units representing the consonants and vowels of the ...
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Multi Prototype Vector Space Models of Word Meaning
... multi-prototype vector space model for lexical semantics with a single parame- ter K (the number of clusters) that generalizes both prototype (K = 1) and exemplar (K = N , the total number of instances) ...per ...
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Deep Learning-An Upcoming Technology
... next word in a sequence from a local context of earlier words71. Each word in the context is presented to the network as a one-of-N vector, that is, one component has a value of 1 and the rest are ...
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Exploiting Morphological Regularities in Distributional Word Representations
... corresponding word pairs for that rule, we fol- low the approach used by Akhtar et ...find word pairs such as <boy,boys>, <object,objects> and ...
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Latent Vector Weighting for Word Meaning in Context
... particular word, and those contexts are grouped into a number of clusters, representing the different senses of the ...target word are similar if the words they in turn co-occur with are ...original ...
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Conditional Generators of Words Definitions
... modeling vector representations of words are used for conditional generation of corresponding word ...quality word embedding should contain all useful information to reconstruct the ...to word ...
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Word Maturity: Computational Modeling of Word Knowledge
... of word meanings from natural language (as can be evidenced by a broad range of applications from synonym tests to automated essay grading), at rates that resemble those of human learners (Laundauer et al, ...
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A Study on Word Similarity using Context Vector Models
... In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic ...
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