[PDF] Top 20 Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
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Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
... (the graph nodes) and of different types of relations between them (the graph arcs; some relation types are antonymy, hypernymy, deriva- tion, ...distributed word repre- ...learned embeddings) ... See full document
7
Parameter Free Hierarchical Graph Based Clustering for Analyzing Continuous Word Embeddings
... of word embeddings or other large high-dimensional datasets, because the neighborhood and macro vertex relations appear to be connected to semantical relations between the words, particularly on the lower ... See full document
9
Improving Lexical Embeddings with Semantic Knowledge
... network based language model that learns word embeddings by maximizing the probability of raw ...prior knowledge about synonyms from semantic resources; we consider both the Paraphrase ... See full document
6
Whom to Learn From? Graph- vs. Text-based Word Embeddings
... As neural networks are becoming a central tech- nology in natural language processing, interest on distributional semantics, with its vector space models of meaning, has been a driving factor for research on natural ... See full document
11
Exploring and Expanding the Use of Lexical Chains in Information Retrieval
... our lexical chains in a two-phase subroutine called Lexical Synset Chain Extraction ...our knowledge, this module is introducing two novel ...flexible lexical chains, considering ... See full document
6
Learning Bilingual Word Embeddings Using Lexical Definitions
... bilingual word embedding ...of lexical definitions, which are clean lin- guistic knowledge that naturally connects word semantics within and across human ...from lexical defini- tions ... See full document
6
Efficient Graph based Word Sense Induction by Distributional Inclusion Vector Embeddings
... target word, we can also cluster every mention based on its context words, which co-occur in a small ...target word could have tens of thousands of mentions in the corpus of ...prior knowledge ... See full document
11
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
... a word and those appearing in the associ- ated ...like knowledge graphs – in order to improve the embeddings (Wang et ...the embeddings to the resource used and its asso- ciated similarity ... See full document
10
Automatic Summarization
... Frequency, Lexical chains, TF*IDF, Topic Words, Topic Models [LSA, EM, Bayesian].. Graph Based Methods.[r] ... See full document
86
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints
... semantic knowledge into the corpus-based learning of word ...tic knowledge as many word ordinal ranking in- ...sensible word embed- ...tic knowledge can all be represented ... See full document
11
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement
... Distributional word representations are widely used in NLP ...are based on an assumption that words with a similar context tend to have a similar ...context-based embeddings, many researches ... See full document
7
Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge
... pre-trained embeddings (de- rived using window-based contexts) to semantic ...of embeddings to capture relatedness suggested by semantic lex- icons while maintaining their resemblance to the ... See full document
6
Sense Embeddings in Knowledge Based Word Sense Disambiguation
... sense embeddings will be used for expanding the gloss of every sense in WordNet, by con- catenating the gloss of the most related senses, in a similar way than Banerjee and Pedersen (2002)’s Extended Lesk ... See full document
7
Exploration of register dependent lexical semantics using word embeddings
... our knowledge, none of the studies made use of distributional ...mainly based on simple word and text statistics; see, for example, (Lee and Myaeng, 2002), (Amasyal and Diri, ...per word ... See full document
9
Lexical Coherence Graph Modeling Using Word Embeddings
... is based on entity transitions over ...entity graph (Guinaudeau and Strube, 2013) is a graph-based, mainly unsuper- vised interpretation of the entity ...projection graph to quantify ... See full document
10
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 ... See full document
11
Hand Crafting a Lexical Network With a Knowledge Based Graph Editor
... microscopic lexical rules that are associated with each lexical ...pull lexical function links from the headword—at the bottom, right above the Comments zone—and the corresponding article-view—on ... See full document
18
A Resource Free Evaluation Metric for Cross Lingual Word Embeddings Based on Graph Modularity
... to word embeddings. Metrics based on word co-occurrences have been developed for measuring the monolingual coher- ence of topics (Newman et ...ric based on co-occurrences across ... See full document
11
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction
... For our experiments we use a subsection of the NewsSpike corpus (Zhang and Weld, 2013). For purposes of efficiency and ease of evaluation we decided to build a KG of around 15k nodes (size in line with the FB15k dataset ... See full document
5
Fusing Document, Collection and Label Graph based Representations with Word Embeddings for Text Classification
... Term weighting schemes. A core aspect in the Vector Space Model for document representation, is how to determine the importance of a term within a document. Many criteria have been introduced with the most prominent ones ... See full document
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