[PDF] Top 20 Distributed Representations of Words to Guide Bootstrapped Entity Classifiers
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Distributed Representations of Words to Guide Bootstrapped Entity Classifiers
... the distributed vector representations of words to expand training data for entity classifiers in a boot- strapped system (see Algorithm ...an entity classifier, we first learn ... See full document
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Distributed Representations of Words and Documents for Discriminating Similar Languages
... In this work we evaluate the 2015 shared task by adapting the approach presented in (Franco- Salvador et al., 2015). We first use an approach based on distances with language prototypes to determine the language group, ... See full document
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Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words
... bag-of- words” model (Mikolov et ...ing distributed representations in language mod- els (Collobert and Weston, 2008) is shown on the ...word representations are created based on contextual ... See full document
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Unsupervised Text Normalization Using Distributed Representations of Words and Phrases
... learn distributed representations of words to capture the notion of contextual similarity and subsequently learn normalization lex- icons from these representations in a com- pletely ... See full document
9
Inducing Crosslingual Distributed Representations of Words
... crosslingual distributed representations for a pair of ...the representations in both languages as we learn them. Words in either language that are similar to each other end up being “close” ... See full document
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Distributed Representations of Mongolian Words and Its Efficient Estimation
... of words as continuous vectors has a long ...of words in ...the words, The output layer is a classifier with the goal of maximizing the probability of the current ...frequent words and ... See full document
7
Argument Extraction from News
... exploits distributed representations of words, extracted from a large non-annotated ...that distributed representations can contribute positively to the task of argument ex- ... See full document
11
Discovering Implicit Knowledge with Unary Relations
... are entity pairs, and will only be supported by a textual relation if they occur in a sentence ...used distributed representations for words or entities and used a model to predict the ... See full document
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Distributional Representations of Words for Short Text Classification
... To get word representation, each input word token is transformed into a vector by looking up word em- beddings learned from language model (Zeng et al., 2014). Distributed representations of words in ... See full document
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Multimodal Semantic Learning from Child Directed Input
... learning distributed representations from child- directed speech (Baroni et ...learns distributed rep- resentations from multimodal child-directed ...induces distributed representations ... See full document
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Reverse engineering Language: A Study on the Semantic Compositionality of German Compounds
... lion words German APA news corpus and discov- ered that compounds account for 47% of the word types but only 7% of the overall token count, with 83% of compounds having a corpus frequency of 5 or ...the ... See full document
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A Comparison of Word based and Context based Representations for Classification Problems in Health Informatics
... Distributed representations (also known as ‘em- beddings’) are dense, real-valued vectors that cap- ture semantics of concepts (Mikolov et ...related words are expected to be closer than those of ... See full document
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Combining Distributed Vector Representations for Words
... Generally, in the continuing discussion about the relative merits of count-based and prediction-based methods (Baroni et al., 2014), the present work sug- gests that there may not be a need to choose. By combining both ... See full document
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Toward Socially Infused Information Extraction: Embedding Authors, Mentions, and Entities
... The user-entity composition model is good at han- dling highly ambiguous mentions. For example, our full model successfully disambiguates entities for mentions such as ‘Sox’ (Boston Red Sox vs. Chicago White Sox), ... See full document
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Learning Dense Representations for Entity Retrieval
... We acknowledge that most of the components of our work are not novel in and of themselves. Dual encoder architectures have a long history (Bromley et al., 1994; Chopra et al., 2005; Yih et al., 2011), including for ... See full document
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Building Compact Entity Embeddings Using Wikidata
... frequent words in the training data using a fixed subsampling rate to enhance the representations of rare ...target words from randomly selected noise words by giving the real target ... See full document
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An LSTM CRF Based Approach to Token Level Metaphor Detection
... Rai et al. (2016) used Conditional Random Fields (CRF) to detect metaphors in an open domain text. For their experiments, they used Syntactic features, Conceptual features, Affective Features and Contextual features. ... See full document
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Jointly Learning Entity and Relation Representations for Entity Alignment
... for entity alignment by jointly modeling entities and rela- tions of ...high-quality entity and relation ...joint entity representations that contain both relation informa- tion and ... See full document
10
Dynamic Entity Representations in Neural Language Models
... one entity. Entity-related ...recover entity clusters, though they only model entity mentions as containing a single word (an inappropriate assumption, in our ...Their entity updating ... See full document
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Entity Mention Detection using a Combination of Redundancy-Driven Classifiers
... Entity Mention Detection (EMD) is an extension of Named Entity Recognition (NER), the task of recognizing different types of entities mentioned in a text. While NER systems are required to identify only ... See full document
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