[PDF] Top 20 Improved Word Embeddings with Implicit Structure Information
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Improved Word Embeddings with Implicit Structure Information
... building word vectors were ...learning word embeddings from raw textual data, popularized via the Word2Vec ...useful word embeddings, but it is also efficient for training and scales ... See full document
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Domain Adapted Word Embeddings for Improved Sentiment Classification
... KCCA embeddings are expected to perform better than the others be- cause CCA/KCCA provides an intuitively bet- ter technique to preserve information from both the generic and DS ...based embeddings ... See full document
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Domain Adapted Word Embeddings for Improved Sentiment Classification
... monolingual word embed- dings across data sets in different application do- mains/contexts for the purpose of a given down- stream task such as sentiment ...ing word embeddings across different ... See full document
9
Improve Chinese Word Embeddings by Exploiting Internal Structure
... than word also provide rich semantic ...in word, Chinese radicals in ...Chinese word and char- acter embeddings (Chen et ...a word into account when modeling the semantic meaning of the ... See full document
10
Verb Argument Structure Alternations in Word and Sentence Embeddings
... complementary word-level and sentence-level datasets, LaVA and FAVA, cover- ing five ...verb embeddings to distinguish which syntactic frames a verb can evoke and which it ...sentence embeddings as ... See full document
11
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction
... exploit word co- occurrence ...this information directly in the features of the word vector ...in word representation learning (Bengio et ...learn word vectors that maximize a language ... See full document
10
CUNI x ling: Parsing Under Resourced Languages in CoNLL 2018 UD Shared Task
... input word forms, which it by default trains jointly with training the parser, ...be improved by pre-training the word embeddings on larger mono- lingual data, and using these fixed ... See full document
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Better Word Embeddings by Disentangling Contextual n Gram Information
... Pre-trained word vectors are ubiquitous in Natural Language Processing ...training word em- beddings jointly with bigram and even trigram embeddings, results in improved unigram em- ... See full document
7
What do we need to know about an unknown word when parsing German
... tag information to isolate the effect of the different techniques for handling ...POS information, we now see a significant ...head word for each compound increases UAS and LAS by ...pound ... See full document
7
Improving Implicit Discourse Relation Recognition with Discourse specific Word Embeddings
... on word pairs in previous work do not work well because of the data sparsity ...use word embeddings (aka distributed representations) in- stead of words as input features, and design vari- ous neural ... See full document
6
Deep Multilingual Correlation for Improved Word Embeddings
... useful information from both x and y. We consider the two input monolingual word em- beddings as different views of the same latent se- mantic ...multilingual information into word embed- ... See full document
7
Semantic Information Extraction for Improved Word Embeddings
... content word in the ...distinct word forms have distinct vec- tors without any overlap, which means that the vec- tor similarities for any two distinct individual word forms will fail to reflect any ... See full document
8
Cross Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon
... trained word embeddings for English and Italian (Bojanowski et ...Every word is represented as an n-grams of characters, for n training between 3 and ...FastText embeddings is important for ... See full document
11
Derivational Morphological Relations in Word Embeddings
... more information about derivations than NMT ...more information in the embeddings if they do not utilize the attention ...less information is stored in the embeddings by the Transformer ... See full document
8
Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization
... polysemous word and this task requires one to identify the meanings of “run” and “operate” are similar to each other when taking “people” as their subject and “company” as their ... See full document
11
Incorporating Subword Information into Matrix Factorization Word Embeddings
... the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence ... See full document
6
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity
... (Moro et al., 2014), a state-of-the-art WSD and Entity Linking algorithm based on BabelNet’s se- mantic network. Babelfy first models each con- cept in the network through its corresponding “se- mantic signature” by ... See full document
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Sub Word Similarity based Search for Embeddings: Inducing Rare Word Embeddings for Word Similarity Tasks and Language Modelling
... In contrast, Soricut and Och (2015) applied an automatic method to induce morphological rules and transformations as vectors in the same embedding space. More specifically, they exploited automatically- learned prefix- ... See full document
10
Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
... dependency-based word embeddings preformed much worse than other meth- ...dependency-based word embeddings catch less topic related information than ...rich information about ... See full document
16
Morphological Word Embeddings
... each word is to its neighbors, where distance is measured in the Hamming distance between morphological ...Morph-LBL embeddings generally encode mor- phology better than the ... See full document
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