[PDF] Top 20 Task Oriented Learning of Word Embeddings for Semantic Relation Classification
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Task Oriented Learning of Word Embeddings for Semantic Relation Classification
... trained embeddings by defin- ing parent and child nodes in dependency trees as ...feature embeddings induced by parsing a large unannotated corpus and then learning em- beddings for the manually ... See full document
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Social Image Tags as a Source of Word Embeddings: A Task oriented Evaluation
... chine learning techniques to integrate/combine visual features with linguistic ...generating word embeddings, and argues that these generated representa- tions exhibit somewhat different and ... See full document
5
Discourse Relation Sense Classification Using Cross argument Semantic Similarity Based on Word Embeddings
... Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Cor- rado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael ... See full document
8
CogALex V Shared Task: GHHH Detecting Semantic Relations via Word Embeddings
... Shared Task on Corpus-Based Identification of Semantic ...for Task-1 and second place for ...for Task-1 on detecting semantic similarity, and ...for Task-2 on identifying ... See full document
6
Word Embeddings as Metric Recovery in Semantic Spaces
... Continuous word representations have been remarkably useful across NLP tasks but re- main poorly ...ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, ... See full document
14
Learning Bilingual Sentiment Word Embeddings for Cross language Sentiment Classification
... bedding learning into a unified process, and out- performs Chen et ...embedding learning process and the in- tegration of semantic and sentiment ... See full document
11
Exploiting Task Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
... The task of abbreviation disambiguation in biomedical documents has been studied by various researchers using supervised machine learning al- gorithms (Liu et ...our task since intensive care ... See full document
6
AutoExtend: Combining Word Embeddings with Semantic Resources
... learn embeddings. In contrast, we can “Auto- Extend” any set of given word embeddings—without (re)training ...existing word embeddings and combining them with a lexical ...existing ... See full document
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Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints
... Typically, word vectors are learned based on the distributional hypothesis (Harris, 1954; Miller and Charles, 1991), which assumes that words with a similar context tend to have a similar ...each word in ... See full document
11
Vector space semantics with frequency driven motifs
... for semantic com- position, and Goyal et ...While word embeddings and lan- guage models from such methods have been use- ful for tasks such as relation classification, polarity ... See full document
10
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
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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
... as word embeddings have been shown effective in capturing fine-grained semantic relations and syntactic regularities in large text corpora ...deep learning models such as those for text ... See full document
16
On the contribution of word embeddings to temporal relation classification
... Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal ...the semantic content of the event ... See full document
11
A Prism Module for Semantic Disentanglement in Name Entity Recognition
... in word embeddings (Bengio et ...entangled word embeddings can be replaced with distributed representations of dis- entangled semantic ... See full document
5
Learning Word Meta Embeddings
... to learning metaembeddings from embeddings is the MVLSA method that learns powerful embeddings directly from multi- ple data sources (Rastogi et ...i.e., embeddings that are exclusively based ... See full document
10
Simple task specific bilingual word embeddings
... their embeddings on document classification and machine translation, and not yet structured pre- diction tasks like POS/SuS tagging or syntactic pars- ...use word alignments, but they still use ... See full document
5
Adjusting Word Embeddings with Semantic Intensity Orders
... better semantic representations, different approaches using semantic lexicons as well as lex- ical knowledge to adjust word vectors have re- cently been ...each word vector to be in the middle ... See full document
8
Semi-Supervised Multi-Task Word Embeddings
... Distributed word representations have shown good perfor- mance for tasks in natural ...the semantic space. Hence, meta-embedding combines multiple word embeddings to increase the cover- age ... See full document
9
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
Searching for the X Factor: Exploring Corpus Subjectivity for Word Embeddings
... Subjectivity Classification Task This task classifies a sentence into subjective or ...This task is prob- ably less sensitive to the subjectivity within word embeddings than ... See full document
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