[PDF] Top 20 Beyond Context: A New Perspective for Word Embeddings
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Beyond Context: A New Perspective for Word Embeddings
... ter by evaluating analogies on datasets with mis- spellings, and, (iv) offers a better feature represen- tation for an extrinsic evaluation of text classifi- cation. The overall goal of our experiments is to understand ... See full document
11
The Role of Context Types and Dimensionality in Learning Word Embeddings
... i.e., context words within some fixed distance of each side of the target ...modified-context embeddings in dependency parsing and lexical substitution ...diverse context types on a wide range ... See full document
11
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... with word embeddings is by Sugawara(Sugawara et ...on context word embeddings (CWE) are merged, and they are used for training a classifier and ...the word in the ...This ... See full document
7
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
... pre-trained word embed- dings outperform random word embeddings by a large ...pre-trained word embeddings are highly use- ful for text classification (Iyyer et ...DEPS context ... See full document
11
Directional Skip Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
... of word orders (Bansal et al., 2014; Ling et al., 2015a), learn- ing context words with different weights (Ling et ...learning word embeddings in an end-to-end unsu- pervised manner without ... See full document
6
Beyond Bilingual: Multi sense Word Embeddings using Multilingual Context
... Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP ...a word, by ex- ploiting crosslingual signals to aid sense ...multi-sense ... See full document
10
Towards Incremental Learning of Word Embeddings Using Context Informativeness
... as word em- beddings (Bengio et ...humans’ word learning abilities by constructing reasonable word representations from limited observations (Lazaridou et ...generating word embed- dings from ... See full document
7
Learning Word Embeddings without Context Vectors
... We train word embeddings on the English Wikipedia dump. We preprocess this dump us- ing gensim.corpora.wikicorpus package 3 and take a subsample of 100K articles. Our corpus consists of approximately 175M ... See full document
6
Multimodal Frame Identification with Multilingual Evaluation
... trained word embeddings as input (Hermann et ...predicate context. The dimension- wise mean of the pretrained embeddings of all words in the sentence is taken as the ...predicate ... See full document
11
Querying Word Embeddings for Similarity and Relatedness
... popular word embedding model with two final sets of (word/context) ...of word and context embeddings as word vectors) could be simulated in a count- based model that ... See full document
10
Dependency Based Word Embeddings
... with parts-of-speech using the Stanford tagger (Toutanova et al., 2003) and parsed into labeled Stanford dependencies (de Marneffe and Man- ning, 2008) using an implementation of the parser described in (Goldberg and ... See full document
7
Generative Topic Embedding: a Continuous Representation of Documents
... generative word em- bedding method PSDVec, which is the precur- sor of ...a word given its context words can be factorized approximately into inde- pendent log-bilinear ...the word ... See full document
10
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
... the context-to-context sim- ilarity metric, we took the set of contexts for the target lemma add from the training set of Senseval-3 (Mihalcea et ...‘query’ context from that set and the other two ... See full document
11
UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging
... The Universal Dependencies project (Nivre et al., 2016) seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for many languages. In 2017 and 2018 CoNLL Shared Tasks Multilingual ... See full document
9
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
6
Word Context Character Embeddings for Chinese Word Segmentation
... incorporate word embedding features in the neural CWS, pre-training the word embeddings in the large-scale labeled ...containing word information, yet obtaining higher F1 ... See full document
7
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations
... Contextualized word representations are replac- ing word vectors in many natural language pro- cessing (NLP) tasks such as sentiment analysis, coreference resolution, question answering, tex- tual ... See full document
7
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
... its word embedding (numer- ical ...next word together with its context (by means of the word embedding representation), in the hope of achieving improved ... See full document
7
Urdu Word Embeddings
... ‘word embeddings’. These embeddings have led to breakthroughs in performance in multiple natural language processing applications, and also hold the key to improving natural language processing for ... See full document
5
Robust Word Vectors: Context Informed Embeddings for Noisy Texts
... without word embeddings — vectors encode semantic and syntactic prop- erties of individual words (Arora et ...these word vectors the user in- put should be clean ...a word vector model trained ... See full document
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