Word Embedding
Tracing armed conflicts with diachronic word embedding models
... In this paper, we make use of a social science dataset which to the best of our knowledge has not been introduced in the NLP field before. This dataset is described in section 3 and comprises a manually annotated history ...
6
Syntactic Interchangeability in Word Embedding Models
... Our investigation focused on a specific relation between words, namely whether they share a part of speech. Many other relations are of interest to the NLP community, such as syntactic dependency relations, and semantic ...
7
Indra: A Word Embedding and Semantic Relatedness Server
... cess word vectors and to find nearest neighbours ...popular word-embedding tool- kits, mainly credited to its efficient implementation of near- est neighbours function ( ˇ Reh˚uˇrek and Sojka, ...
7
Understanding Undesirable Word Embedding Associations
... undesirable word associations in em- bedding ...of word embedding association, has the- oretical flaws that cause it to systematically over- estimate ...any word can be classified as ...
10
A Hybrid Learning Scheme for Chinese Word Embedding
... target word. The information in this type of model works competitively for word em- ...learn word embedding and give good estimation for rare and unseen ...
7
Application of Word Embedding to Drug Repositioning
... Word embedding for distributed representation of word sense is a new approach to this ...by word em- bedding represents word senses and distances (similarities) between them quite ...of ...
10
Word Embedding for Response To Text Assessment of Evidence
... skip-gram embedding and Baroni’s embedding, there are many ...gram embedding partially solves the tense prob- lem, Baroni’s embedding solves it better because it has a larger training ...
7
Multi Granularity Chinese Word Embedding
... Word embedding, also known as distributed word representation, is to represent each word as a real- valued low-dimensional vector, through which the semantic meaning of the word can be ...
6
Segmentation Free Word Embedding for Unsegmented Languages
... train word embedding models on a raw text corpus, we have to do word segmentation as a preprocessing ...supervised word segmentation (Kudo et ...
6
Tweaks and Tricks for Word Embedding Disruptions
... Word embedding models are vector-spaces in which words are represented as points in an N- dimensional Euclidean ...the embedding models, the main concern remains weights ...
5
Word Embedding Approach for Synonym Extraction of Multi Word Terms
... the embedding vector of the phrase Volga river is similar to the addition of the embedding vector of Volga and the embedding vector of river ...that word embbeding models exhibit offers key ...
7
Exploring Word Embedding for Drug Name Recognition
... uses word embedding features to recognize drug names from biomedical ...as word vectors and word clusters generated by the Word2Vec tool and a lexicon feature from the DINTO ...using ...
9
Using word embedding for bio event extraction
... using word embedding only while uti- lizing the exactly same extraction infrastructure, which is a pipeline converging tokenization, pars- ing and other pre-processing upon Apache ...use word ...
6
Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation
... Word embedding has been found to be high- ly powerful to translate words from one lan- guage to another by a simple linear transfor- ...the word vec- tors on a hypersphere and constrains the lin- ear ...
6
Improving Text Classification with Word Embedding
... et al. [8] used Word2Vec for the first story detection by learning the word embedding from Twitter messages. Jiang, et al. [9] combined Neural Network Language Models and Word2Vec to enhance the sentiment ...
51
CogniVal: A Framework for Cognitive Word Embedding Evaluation
... cognitive word embedding evaluation framework. The vectorized word representa- tions are evaluated by using them to predict eye- tracking or brain activity data recorded while par- ticipants were ...
12
Revisiting Word Embedding for Contrasting Meaning
... Word Embedding Word embedding models learn continuous representations for words in a low di- mensional space (Turney and Pantel, 2010; Hin- ton and Roweis, 2002; Collobert et ...neighbor ...
10
A Simple Word Embedding Model for Lexical Substitution
... The lexical substitution task requires identify- ing meaning-preserving substitutes for a tar- get word instance in a given sentential context. Since its introduction in SemEval-2007, vari- ous models addressed ...
7
Word Node2Vec: Improving Word Embedding with Document Level Non Local Word Co occurrences
... Standard word embedding algorithms, such as word2vec and Glove, make a restric- tive assumption that words are likely to be semantically related only if they co-occur lo- cally within a window of fixed ...
11
A Joint Model for Word Embedding and Word Morphology
... Word embedding models associate each word in a corpus with a vector in a semantic ...Current word embedding models treat words as ...and word embed- ding models suffer from the ...
9