[PDF] Top 20 Word Mover’s Embedding: From Word2Vec to Document Embedding
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Word Mover’s Embedding: From Word2Vec to Document Embedding
... SL999(PSL) word vectors (Wieting et ...PSL word vec- tors; 2) Deep Averaging Network (DAN) (Iyyer et ...GloVe word vectors; 3) tf-idf : a weighted average of GloVe word vecors using TF-IDF ... See full document
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k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific Text
... area from the ACM hierarchy and the Wikipedia Machine Learning outline, and we define a test set of analogy questions as semantic questions following the rela- tion described ...the word “unsupervised ... See full document
15
Beyond Word2Vec: Embedding Words and Phrases in Same Vector Space
... Word embeddings are being used for sev- eral linguistic problems and NLP tasks. Improvements in solutions to such prob- lems are great because of the recent break- throughs in vector representation of words and ... See full document
7
Word Embedding Evaluation and Combination
... of word embed- dings coming from different available implementations: word2vec (Mikolov et ...instance, word2vec embeddings (Mikolov et ...dependency-based word embeddings (Levy and ... See full document
6
Query Expansion with Locally Trained Word Embeddings
... trieval from the larger Gigaword corpus led to substantial ...local embedding is trained using a larger variety of topical ma- terial than if it were to use a retrieval from the smaller target ...An ... See full document
11
Diachronic degradation of language models: Insights from social media
... using word embedding models trained on Twitter corpora, such as Twit- ter GloVe and Word2Vec (Mikolov et ...about word embeddings to better understand semantic drift in terms of the adjective ... See full document
6
Word Embeddings as Metric Recovery in Semantic Spaces
... of word embeddings has prompted a parallel body of work that seeks to better understand their properties, associated estimation al- gorithms, and explore possible ...with word2vec extend to other ... See full document
14
Using Embedding Masks for Word Categorization
... on word co- occurrences can be computed using various mech- anisms and ...topics from a large collection of texts, while newer methods make use of neu- ral embeddings to train word representations on ... See full document
5
Quantum Inspired Complex Word Embedding
... of word dependency ...a word or a text segment being a real-valued vec- tor or matrix, largely due to the lack of proper textual features corresponding to the imaginary ... See full document
8
Application of Word Embedding to Drug Repositioning
... Although word analogy was not available, word2vec provided significant advantage in the text mining from a large number of biomedical texts in this ...traditional word frequency and vector ... See full document
10
Linear Ensembles of Word Embedding Models
... all word embed- ding systems learn slightly different embeddings, combining for instance Word2Vec (Mikolov et ...ferent word embeddings can be ...of word embeddings constructed based on dif- ... See full document
9
Download Download PDF
... for embedding document metadata, and potentially other semantic references inline in word processing documents, which the authors have implemented with the help of a software development ...Microsoft ... See full document
14
Word Node2Vec: Improving Word Embedding with Document Level Non Local Word Co occurrences
... show word- node2vec results with optimal parameter ...model document-level co-occurrences as seen from the fact that the ρ and accuracy values decrease as α gets close to 0 or 1 (the 1st and 3rd ... See full document
11
Evaluating distributed word representations for capturing semantics of biomedical concepts
... Such word vector representations, also known as word embedding, have been shown to improve the performance of machine learning models in several NLP ...state-of-the-art word embedding ... See full document
6
Word and Document Embedding with vMF Mixture Priors on Context Word Vectors
... the word vectors that are ob- tained by standard models is essentially ...ing word embeddings from a theoretical point of view (Arora et ...words from the same part-of-speech class should be ... See full document
10
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
Density Matching for Bilingual Word Embedding
... pre-trained word embeddings as fixed train- ing samples, at every training step we obtain sam- ples from the Gaussian mixture ...entire embedding space instead of only the specific points assigned ... See full document
11
Embedding a Semantic Network in a Word Space
... Evaluating intrinsically using e.g. a correlation be- tween a graph-based similarity measure and geo- metric similarity would be problematic, since this is in some sense what our algorithm optimizes. We therefore ... See full document
6
Syntactic Interchangeability in Word Embedding Models
... obtained from the human- annotated scores for each benchmark, we can find the enrichment of interchangeable pairs among related ...each word in each benchmark pair (tagging them in isolation to select the ... See full document
7
Multi Granularity Chinese Word Embedding
... capturing finer-grained semantics that are more spe- cific to the word. The top words discovered by them are semantically related to “ 游泳 (swimming)” it- self, e.g., “ 潜泳 (underwater swimming)” and “ 爬 泳 (front ... See full document
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