[PDF] Top 20 Joint Semantic and Distributional Word Representations with Multi Graph Embeddings
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Joint Semantic and Distributional Word Representations with Multi Graph Embeddings
... This dual representation becomes even more im- portant when considering graph embeddings. To find a self-supervised optimization function that induces a representation of nodes, two different goals are ... See full document
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Vectors or Graphs? On Differences of Representations for Distributional Semantic Models
... Sense Representations Another consequence of the metric space is that neighbourhoods of lexical items are populated with similar lexical items across all frequency ...induce word senses: Suppose we ... See full document
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Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
... another Graph Con- volution based framework, SemGCN, for incor- porating semantic knowledge in pre-trained word ...labeled graph with words as nodes and edges representing semantic ... See full document
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A Comparison of Vector based Representations for Semantic Composition
... of distributional representation and their effect on semantic ...simple distributional semantic space (Mitchell and Lapata, 2010), word embeddings computed with a neural language ... See full document
11
Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs
... the word-to-word translation of MWEs often results in wrong trans- lations (Piao et ...applied distributional represen- tations of words and phrases in SMT (Mikolov et ... See full document
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What a neural language model tells us about spatial relations
... what semantic knowledge about spatial relations is captured in representations of a generative neural language ...between multi-word spatial relations at two levels: the word ... See full document
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Improving Distributional Similarity with Lessons Learned from Word Embeddings
... word2vec embeddings to the more tradi- tional distributional methods, such as pointwise mutual information (PMI) matrices (see Turney and Pantel (2010) and Baroni and Lenci (2010) for comprehensive ...a ... See full document
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Embedding Semantic Relations into Word Representations
... Learning representations for semantic relations is important for various tasks such as analogy de- tection, relational search, and relation classifica- ...learning representations for individual ... See full document
7
Learning Semantic Hierarchies via Word Embeddings
... on word em- beddings. Word embeddings, also known as dis- tributed word representations, typically represent words with dense, low-dimensional and real- valued ...vectors. Word ... See full document
11
Semantic Information Extraction for Improved Word Embeddings
... “one-hot” representations, which al- locate a separate dimension in the vector space for every content word in the ...distinct word forms have distinct vec- tors without any overlap, which means that ... See full document
8
Adjusting Word Embeddings with Semantic Intensity Orders
... using semantic intensity information from other linguistic ...use word definitions from ...analyzing word definitions, we can obtain word intensity ... See full document
8
Learning Better Embeddings for Rare Words Using Distributional Representations
... Second, to simplify our experimental setup and make the number of runs mangeable, we used the parameter θ both for corpus processing (only θ oc- currences of a particular word were left in the cor- pus) and as the ... See full document
6
Joint Learning of Sense and Word Embeddings
... tificial word in a corpus, we replace all occurrences of four ...each word (fourth, fifth, sixth and seventh column) using both labelled and unlabelled corpora, unlike the mixture of the various senses ... See full document
7
Cross lingual Semantic Specialization via Lexical Relation Induction
... ized word embeddings on DST, we rely on the Neural Belief Tracker (NBT) v2 (Mrkˇsi´c and Vuli´c, 2018): it is a fully statistical DST model that op- erates solely on the basis of pretrained word vec- ... See full document
12
Real Multi Sense or Pseudo Multi Sense: An Approach to Improve Word Representation
... multiple representations for polysemous words can improve the performance of word embeddings on many ...a word may actually point to the same meaning, namely pseudo ...pseudo ... See full document
10
Siamese CBOW: Optimizing Word Embeddings for Sentence Representations
... sentence embeddings. Quality should manifest itself in embeddings of semantically close sentences being similar to one another, and embeddings of semantically different sentences being ...the ... See full document
11
Semantic Similarity of Arabic Sentences with Word Embeddings
... each word in each ...the word provides, that is, whether the term that occurs infrequently is good for discriminating between documents (in our case ... See full document
7
Word Embeddings as Metric Recovery in Semantic Spaces
... current word embedding algorithms build on the distributional hypothesis (Harris, 1954) where similar contexts imply similar meanings so as to tie co-occurrences of words to their underlying mean- ...free ... See full document
14
AutoExtend: Combining Word Embeddings with Semantic Resources
... the word constraint (Equation (29)) divided by the number of words, the lexeme constraint (Equation (33)) divided by the number of lexemes, and the similarity constraint (Equation (34)) divided by the number of ... See full document
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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
... dependency-based word embeddings had the worse performance among the ...of word embeddings may vary across different domains and differ- ent text ...different word embeddings ... See full document
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