[PDF] Top 20 Combining Distributed Vector Representations for Words
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Combining Distributed Vector Representations for Words
... in distributed vector represen- tations for words has resulted in an increased diversity of approaches, each with strengths and ...verse vector representations may be inexpen- sively ... See full document
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Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words
... over distributed representations of words. The distributed word representations are obtained by using a log-linear model and we model the low-dimensional seman- tic vector space ... See full document
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Unsupervised Text Normalization Using Distributed Representations of Words and Phrases
... bag-of- words model (Mikolov et ...ing distributed representations in language mod- els (Collobert and Weston, 2008) is shown on the ...word representations are created based on contextual ... See full document
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Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
... are distributed representations in a vector ...those vector representations, with word2vec (Mikolov et ...of words (CBoW) and skipgram, both consisting of a single hidden layer ... See full document
7
Learning Semantically and Additively Compositional Distributional Representations
... distributional vector representations are useful in capturing lexical semantics of words (Turney and Pantel, 2010; Levy et ...in combining the word vectors to form meanings of ... See full document
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Distributed Vector Representations for Unsupervised Automatic Short Answer Grading
... Grading is an important task in schools and colleges in order to assess students’ understanding and guide teachers in providing instructive feedback. However, answer grading is tedious work and the prevalence of Computer ... See full document
10
Distributed Representations of Mongolian Words and Its Efficient Estimation
... Abstract. The word vectors has good semantic properties that can be used to improve and simplify many natural language processing applications. In this paper, we use the two model architectures Continuous ... See full document
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Discovering Implicit Knowledge with Unary Relations
... on combining the matrix factorization approach of universal schema (Riedel et ...used distributed representations for words or entities and used a model to predict the relation between two ... See full document
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A Comparison of Word based and Context based Representations for Classification Problems in Health Informatics
... Distributed representations (also known as ‘em- beddings’) are dense, real-valued vectors that cap- ture semantics of concepts (Mikolov et ...related words are expected to be closer than those of ... See full document
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Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
... contexts, words with similar meaning are embedded in similar locations in vector space as a by-product of the pre- diction task (Mikolov et ...target words, a vector unique to the document is ... See full document
6
Distributed Representations of Words to Guide Bootstrapped Entity Classifiers
... vector representations on a combined dataset of a 2014 Wikipedia dump ...removed words that occurred less than 20 times, resulting in a vocabulary of 89k ... See full document
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Distributed Representations of Words and Documents for Discriminating Similar Languages
... the distributed vectors of the combina- tion of words, we used a logistic classifier (Skip- gram + LG ...contain words which are not present in the training ...those words we have not learned ... See full document
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Conditional Generators of Words Definitions
... It is well-known that good language model can of- ten improve metrics such as BLEU for a particu- lar NLP task (Jozefowicz et al., 2016). According to this, we decided to pre-train our models. For this purpose, ... See full document
6
Semantic Word Clusters Using Signed Spectral Clustering
... While vector space models (Turney et al., 2010) such as word2vec (Mikolov et al., 2013), Global vectors (GloVe) (Pennington et al., 2014), or Eigenwords (Dhillon et al., 2015) capture relat- edness, they do not ... See full document
11
Reverse engineering Language: A Study on the Semantic Compositionality of German Compounds
... The newly introduced mask models build upon the idea that when a word w enters a composition pro- cess, there is some variation in its meaning de- pending on whether it is the first or the second el- ement of the ... See full document
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CSE: Conceptual Sentence Embeddings based on Attention Model
... frequent words. This is a good speedup trick because com- mon words are accessed quickly (Le and Mikolov, ...sentence vector, the context vector and the concept vectors could be averaged or ... See full document
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Paraphrase Assessment in Structured Vector Space: Exploring Parameters and Datasets
... In Erk and Padó (2008), we evaluated SVS on two variants of the paraphrase assessment test: first, the pre- diction of human judgments on a seven-point scale for paraphrases for verb-subject pairs (Mitchell and Lap- ata, ... See full document
9
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
... two words given the contexts they occur in. For ex- ample, the words bank and money should have a low similarity score given the contexts: “along the east bank of the river”, and “the basis of all money ... See full document
6
Multimodal Semantic Learning from Child Directed Input
... Several aspects make IFC a challenging dataset. Firstly, we are dealing with language produced in an interactive setting rather than written discourse. For example, compare the first sentence in the Wikipedia entry for ... See full document
6
An LSTM CRF Based Approach to Token Level Metaphor Detection
... Support Vector Ma- chine (Cortes and Vapnik, 1995) (SVM) ...tree representations, they considered word vector, lemma, POS tag, depen- dency label, and WordNet (Fellbaum, 1998) su- persense ... See full document
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