[PDF] Top 20 Lexical Coherence Graph Modeling Using Word Embeddings
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Lexical Coherence Graph Modeling Using Word Embeddings
... a coherence model is readability ...Other coherence models (Barzi- lay and Lapata, 2008; Guinaudeau and Strube, 2013; Mesgar and Strube, 2014) are also evaluated on this ...entity graph (Guinaudeau ... See full document
10
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
... a word and those appearing in the associ- ated ...issue using an attentive model for context selection (Ling et ...the embeddings (Wang et ...the embeddings to the resource used and its asso- ... See full document
10
Graph based Local Coherence Modeling
... a graph and then model local coherence by applying centrality mea- sures to the nodes in the graph (Section ...a graph is a more powerful representa- tion for local coherence than the ... See full document
11
Nonparametric Spherical Topic Modeling with Word Embeddings
... Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine simi- larity. However, ... See full document
6
Improved Semantic Representation for Domain Specific Entities
... curate modeling of domain-specific lexical items which either have low frequencies or are non-existent in open-domain ...improves word embeddings in specific domains by first transforming a ... See full document
5
Joint Semantic and Distributional Word Representations with Multi Graph Embeddings
... modify word embeddings obtained through pure distributional, lexical approaches ...new embeddings should not be too far apart from the original ... See full document
6
Whom to Learn From? Graph- vs. Text-based Word Embeddings
... of lexical information, and the quality of the resulting word embeddings, by as- sessing how graph-based word embeddings com- pare to mainstream text-based ...account ... See full document
11
Reducing Lexical Features in Parsing by Word Embeddings
... parser using embedding features constantly outperforms the ...the word embeddings, even though the word embed- dings are trained on internal states of the baseline parser, which is trained on ... See full document
8
Coherence models in schizophrenia
... to modeling coherence in schizophrenia. The Incoherence model, using TF- IDF sentence embeddings and GloVe word embeddings, was able to distinguish between healthy controls and ... See full document
11
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings
... the lexical chains as a mechanism for generation of Pseudo Corpora ...construct lexical chains over a knowl- edge graph, instead of constructing lexical chains over ...in Word Sense ... See full document
7
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs
... topic modeling with word ...over word embeddings, however, the word weights of topics are measured by the Euclidean ...GloVe word embeddings, and then compare MvTM with ... See full document
10
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
... explore using visual features to aid text extraction from VRDs; however, approaches they proposed are based on a large amount of heuristic knowledge and human-designed features, as well as limited in known ... See full document
8
A Framework for Developing and Evaluating Word Embeddings of Drug named Entity
... domain, word embeddings are primarily used for biomedical named entity recognition (BNER) with evaluations conducted on tasks such as JNLPBA (Kim et ...of word representations (WR) on ... See full document
5
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... Dat Quoc Nguyen, Mark Dras, and Mark Johnson. 2017. A novel neural network model for joint POS tagging and graph-based dependency parsing. In Proceedings of the CoNLL 2017 Shared Task: Mul- tilingual Parsing from ... See full document
19
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym Synonym Distinction
... Antonymy and synonymy represent lexical se- mantic relations that are central to the organization of the mental lexicon (Miller and Fellbaum, 1991). While antonymy is defined as the oppositeness be- tween words, ... See full document
6
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts
... trees using both the neighbour-joining and the UPGMA methods for all the ma- trices described above and compare them with the trees manually created by our ...of using word em- beddings were closest ... See full document
14
Fast Query Expansion on an Accounting Corpus using Sub Word Embeddings
... We present early results from a system un- der development which uses sub-word em- beddings for query expansion in the pres- ence of mis-spelled words and other aberra- tions. We work for a company which cre- ates ... See full document
5
Deconfounded Lexicon Induction for Interpretable Social Science
... Lexicon induction. Some work in lexicon in- duction is intended to help interpret the subjective properties of a text or make make machine learn- ing models more interpretable, i.e. so that prac- titioners can know why ... See full document
11
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations
... debias word embeddings, an appropriate word list representing the bias in question needs to be used to define the ...der word list might be a set of pronouns which are specific to a particular ... See full document
7
Review Extract Using Word Embeddings
... Word embedding is a distributed representation for words in a vector space [5].It has become a popular tool in the field of NLP. It transform words into vectors, and we can evaluate the correlation between two ... See full document
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