[PDF] Top 20 Dict2vec : Learning Word Embeddings using Lexical Dictionaries
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Dict2vec : Learning Word Embeddings using Lexical Dictionaries
... into word embeddings directly from corpora is a difficult ...into embeddings is to use external data. Lexical databases like WordNet or sets of syn- onyms like MyThes thesaurus can be used ... See full document
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Exploration of register dependent lexical semantics using word embeddings
... the dictionaries or the one given by distributional models trained on a full balanced and representative corpus (for example, the whole ...given word as a sequence of its ‘nearest associates’: words closest ... See full document
9
Joint Learning of Sense and Word Embeddings
... the word vector ac- cording to the context, MSSG predicts the nearest sense first, and then updates the gradient of the sense ...of word sense dis- crimination by clustering a word contexts, before ... See full document
7
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
... distributed word repre- ...WordNet. Learning is first performed on each of the two resources, and then various combination methods are ...learned embeddings) to the more complex ... See full document
7
Lexical Comparison Between Wikipedia and Twitter Corpora by Using Word Embeddings
... of word usage within social media. In this paper, we compute a word embedding for a corpus of tweets, compar- ing it to a word embedding for ...After learning a transformation of one vec- tor ... See full document
5
Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings
... of word meanings over time using diachronic text corpora is a relatively niche subject with little commercial applicability, it has recently gained attention in the broader compu- tational linguistics ... See full document
6
Lexical Chains meet Word Embeddings in Document level Statistical Machine Translation
... the lexical chains in the source and next generate the target lexical chains that are used by their cohesion ...target lexical chains, they train MaxEnt classifiers — one per unique source chain ... See full document
11
Using Multi Sense Vector Embeddings for Reverse Dictionaries
... vector embeddings have been ...single-sense word embedding and a lexical resource to induce vectors representing different senses of a ...create embeddings that distinguish between different ... See full document
12
Learning to Respond to Mixed code Queries using Bilingual Word Embeddings
... a word aligns to consecutive multiple ...the word segmentation and realign, in order to derive more 1-1 correspon- ...Chinese word (e.g., “power plant” → “ 發 電 廠 ”) into two pairs of 1-1 word ... See full document
5
Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge
... specialized lexical re- sources with limited availability or is obtained from complex procedures that are difficult to repli- ...monolingual dictionaries as a simple yet effective source of semantic ... See full document
6
Learning Bilingual Word Embeddings Using Lexical Definitions
... Bilingual word embeddings, which represent lexicons of different languages in a shared em- bedding space, are essential for supporting se- mantic and knowledge transfers in a variety of cross-lingual NLP ... See full document
6
Towards Incremental Learning of Word Embeddings Using Context Informativeness
... new word acquisition by an adult speaker who already masters a substantial vocab- ...‘background’ lexical knowledge in the shape of a distributional space acquired over a large text ...novel word is ... See full document
7
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts
... versions using the neighbour-joining method based on the distance matrix computed using the word embeddings based approach they provided us with the best trees for AST ... See full document
14
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym Synonym Distinction
... with lexical resources such as thesauruses or ...that word pairs that occur in the same thesaurus category are close in meaning and marked as synonyms, while word pairs occurring in contrasting ... See full document
6
Domain Adapted Word Embeddings for Improved Sentiment Classification
... combine word embeddings weighted by their term frequency ...DA embeddings obtained by applying KCCA on GlvCC generic and LSA DS embeddings pro- vide the best performing results on all data ... See full document
9
Learning Multilingual Word Embeddings Using Image Text Data
... multilingual embeddings has relied on crosslingual human-labeled data, such as bilingual lexicons (Mikolov et ...in learning bilingual embeddings for En- glish and French, it may be useful to ... See full document
10
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings
... of using French data is mo- tivated by the fact that Algerian dialect is highly code- switched as explained in the ...a word in PADIC has only one way to write it, since the rules used to write PADIC were ... See full document
7
Learning Semantic Hierarchies via Word Embeddings
... method (Fu et al., 2013). This method mines hy- pernyms of a given word w from multiple sources and returns a ranked list of the hypernyms. We select the hypernyms with scores over a threshold of each word ... See full document
11
Learning Word Embeddings without Context Vectors
... Most word embedding algorithms such as word2vec or fastText construct two sort of vec- tors: for words and for ...suggest using indefinite inner product in skip-gram negative sampling ...on word ... See full document
6
ACCURAT Toolkit for Multi Level Alignment and Information Extraction from Comparable Corpora
... bilingual dictionaries to lexically map documents from one language to ...The dictionaries are automatically generated via word alignment using GIZA++ (Och and Ney, 2000) on parallel ...each ... See full document
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