[PDF] Top 20 Learning Task specific Bilexical Embeddings
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Learning Task specific Bilexical Embeddings
... Bilexical operators can be useful for multiple NLP applications. For example, they can be used to reduce ambiguity in a parsing task. Consider the following sentence extracted from a weblog: Vynil can be ... See full document
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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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Learning Tag Embeddings and Tag specific Composition Functions in Recursive Neural Network
... As for TE-RNN/RNTN, the fine-grained accu- racy of TE-RNN is boosted by 4.8% compared with RNN and the accuracy of TE-RNTN by 3.2% compared with RNTN. TE-RNTN also beat the AdaMC-RNTN by 2.2% on the fine-grained clas- ... See full document
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Learning Composition Models for Phrase Embeddings
... phrase embeddings that uses pre-defined compo- sition operators (Mitchell and Lapata, 2008), ...on learning compositions relies on matrices/tensors as transformations (Socher et ...dense embeddings: ... See full document
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MoRTy: Unsupervised Learning of Task specialized Word Embeddings by Autoencoding
... Word embeddings are ubiquitous in Natural Lan- guage ...best embeddings are not yet possible (Bolle- gala and Bao, 2018; Kiela et ...fit specific end-tasks using inductive bias – ...embedding ... See full document
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Learning Sense specific Word Embeddings By Exploiting Bilingual Resources
... our embeddings by feeding them as features to the task of Chinese named entity recognition (NER), which is a simple semi-supervised learning mechanism (Turian et ...sense-specific ... See full document
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Discriminative Phrase Embedding for Paraphrase Identification
... tion task, on one hand contributes to expand- ing deep learning embeddings to include con- tinuous and discontinuous linguistic ...phrases specific to paraphrase task, so that a ... See full document
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Best Practices for Learning Domain Specific Cross Lingual Embeddings
... Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across ...to learning cross-lingual em- beddings is to train monolingual ... See full document
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Semi-Supervised Multi-Task Word Embeddings
... multi-task learning (SS-MTL) embeddings to analogy ...meta- embeddings carry over to analogy even if not all embedding algorithms preserve analogy relations and (2) check if the similarity ... See full document
9
Improving Implicit Discourse Relation Recognition with Discourse specific Word Embeddings
... this way, they represent words in the space of con- nectives to directly encode their discourse func- tion. The major limitation of their approach is that the dimension of the word representations must be less than or ... See full document
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Simple task specific bilingual word embeddings
... of learning bilingual word embeddings, ...word embeddings can potentially be used for better cross-language transfer of NLP mod- els, as we show in this ...word embeddings have defined similar ... See full document
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Joint Multitask Learning for Community Question Answering Using Task Specific Embeddings
... multitask learning of two community Question Answering problems: question-question relatedness and an- swer ...learn task-specific em- beddings, which are then used in a pairwise CRF as part of a ... See full document
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A Multi task Learning Approach to Adapting Bilingual Word Embeddings for Cross lingual Named Entity Recognition
... multi-task learning approach can help adapt bilingual word embeddings (BWE’s) to improve cross-lingual ...be task- specific, and outperforms the baseline of using pre-trained ... See full document
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Leveraging Social Network Data to Alleviate Cold-Start Problem in Recommender Systems
... challenging task to transform the social networking information into latent user features which can be effectively used for product ...In specific, we represent learning both users‟ and products‟ ... See full document
8
Scoring Lexical Entailment with a Supervised Directional Similarity Network
... for learning task-specific trans- formation functions on top of general- purpose word ...from task-specific scores on a subset of the vo- cabulary, our architecture is able to gener- ... See full document
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Detecting Cybersecurity Events from Noisy Short Text
... deep learning methods have shown to be outperform- ing traditional approaches in several NLP tasks (Chen and Manning, 2014; Bahdanau et ...by learning domain-specific word embeddings and ... See full document
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Exploiting Entity BIO Tag Embeddings and Multi task Learning for Relation Extraction with Imbalanced Data
... RNN is also widely used in relation extrac- tion. Miwa and Bansal (2016) used LSTM and tree structures for relation extraction task. Their model is composed of three parts: an embedding layer to encode the input ... See full document
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Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... semi-supervised learning approaches, co-training and self-training are prob- ably the most ...semi-supervised learning techniques were used for word sense ... See full document
10
Learning Bilingual Sentiment Specific Word Embeddings without Cross lingual Supervision
... Sentimental Embeddings Continuous word representations encode the syntactic context of a word but often ignore the information of sentiment ...sentimental embeddings on both languages then aligning them in ... See full document
10
Drop out Conditional Random Fields for Twitter with Huge Mined Gazetteer
... the task-specific gazetteers. Task-specific gazetteers make the mod- els more general and increase their coverage for un- seen ...our task, we first expand gazetteers from knowledge ... See full document
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