[PDF] Top 20 Neural Vector Conceptualization for Word Vector Space Interpretation
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Neural Vector Conceptualization for Word Vector Space Interpretation
... Distributed word vector spaces are consid- ered hard to interpret which hinders the under- standing of natural language processing (NLP) ...a word vector space. To this end, we train a ... See full document
7
Looking for Hyponyms in Vector Space
... the space. Finally, the dependency-based vectors outperform all other vector types, giving ...different vector space models, and we we found these results to be ... See full document
10
Dual Embeddings and Metrics for Relational Similarity
... the word representations learned from neural network models do an especially good job in capturing not only attributional similarities between words but also similarities between pairs of words (Mikolov et ... See full document
7
Improving Vector Space Word Representations Using Multilingual Correlation
... common vector space such that translation pairs (as deter- mined by automatic word alignments) should be maximally correlated ...ferent neural networks, one that models word se- quences ... See full document
10
Efficient Non parametric Estimation of Multiple Embeddings per Word in Vector Space
... learning vector representations of words; here we will describe only those most relevant to understanding this pa- ...on neural language mod- els, proposed by Bengio et al (2003), which extend the ... See full document
11
Deriving Adjectival Scales from Continuous Space Word Representations
... Continuous space word representations ex- tracted from neural network language mod- els have been used effectively for natural lan- guage processing, but until recently it was not clear whether the ... See full document
6
Detecting Compositionality of Multi Word Expressions using Nearest Neighbours in Vector Space Models
... Multi-word expressions (MWEs) are defined as “id- iosyncratic interpretations that cross word bound- aries” (Sag et al., 2002). They tend to have a standard syntactic structure but are often semanti- cally ... See full document
6
Anomaly Detection In Legal Documents Using Machine Learning
... layer neural networks that are trained to reconstruct linguistic contexts of ...a vector space, typically of several hundred dimensions, unique word in the corpus being assigned a ... See full document
5
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
... where the two terms correspond to the log- likelihood of the encoder RNN LM and the de- coder RNN LM, respectively, and α is the hyper parameter which weights the importance of the second term. In our experiments, we let ... See full document
11
Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words
... the vector space represented by the distributed representa- ...from neural networks in hidden Markov model (HMM) based speech recognition (Grezl and Fousek, ...unordered word-pair ... See full document
9
Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
... of word sense disambiguation based on word embeddings, su- pervised and ...probabilistic interpretation of embeddings and computes log probability from sequences of word embedding ...ambiguous ... See full document
6
A Structured Vector Space Model for Word Meaning in Context
... The SVS scheme we have proposed incorporates syntactic information in a more general manner than previous models, and thus addresses the issues we have discussed in Section 1. Since the representation retains individual ... See full document
10
Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
... center word from neighbor words whereas skip-gram gets the representation of neighbor words from a center ...on word order, because their objective function is to maximize the probability of occurrence of ... See full document
6
Cross Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
... learning word embeddings can be ap- plied to this challenging syntactic-semantic ...bilingual vector space with English, jointly specialising the representa- tions to encode the relational ... See full document
13
Using Deep Learning Technique To Query Relational Data Using Multi-Lingual Query Generator And Translator With NLP Support
... The author develops the system according to the rules that meet the needs of users by accepting Hindi as a search term with results shown in Hindi only. Savvy, which is a general application pattern matching frameworks ... See full document
6
Projections and Reflections in Vector Space
... We study projections onto a subspace and reflections with respect to a subspace in an arbitrary vector space with an inner product. We give necessary and sufficient conditions for two such transformations ... See full document
5
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
... 3.2 Linguistic Constraints via Regularization The nearest neighbors generated using distribu- tional word vectors tend to contain a mixture of synonyms, hypernyms, co-hyponyms and other re- lated words ... See full document
8
Sparse Overcomplete Word Vector Representations
... distributional word vectors (Yu and Dredze, 2014; Xu et ...a word) having a small number of active dimensions (Olshausen and Field, 1997; Lewicki and Sejnowski, 2000), and to increase stability in the ... See full document
10
Correlations between Word Vector Sets
... exceeds that of more expensive or offline methods like that of Arora et al. (2017), which performs PCA computations on the entire test set. Addi- tionally, while the multivariate correlation meth- ods such as CKA are ... See full document
11
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 ... See full document
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