[PDF] Top 20 Aggression Identification and Multi Lingual Word Embeddings
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Aggression Identification and Multi Lingual Word Embeddings
... Despite the fact that data was divided by language, we noticed that some Hindi vocabulary appeared in the English data. This raised an interesting question. Could such vocabulary be conveying information regarding the ... See full document
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Cross lingual complex word identification with multitask learning
... Hypernym chain As a measure of semantic specificity, we further consider the length of the hypernym chain of an item, i.e. the number of hypernyms that can recursively be obtained for a word. These are also ... See full document
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A Comparison of Word Embeddings for English and Cross Lingual Chinese Word Sense Disambiguation
... the word “little” in “These are serious issues and themes, and sometimes little kids aren’t ready to process and understand these ideas”, Bing Translator provides a translation of “ 这 些都是严 重 的问 题 和主 题 ，有 时 小 小 小 孩 ... See full document
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Weakly Supervised Concept based Adversarial Learning for Cross lingual Word Embeddings
... Different previous pieces of work on bilingual lex- icon induction use different datasets. We choose two from the publicly available ones, selected to have a comprehensive evaluation of our method. BLI-1 The dataset ... See full document
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Cross Lingual Word Representations via Spectral Graph Embeddings
... The goal of CL-Eigenwords is to construct vec- tor representations of words of two (or more) languages from multilingual corpora at the same time. This problem is formulated as an example of Cross-Domain Matching ... See full document
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Cross Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon
... a word is the number of similarly spelled words, within and across languages (John- son and Pugh, 1994; van Heuven et ...or multi-lingual, is a space of distributed word representations where ... See full document
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Cross Lingual Word Embeddings for Low Resource Language Modeling
... cross- lingual word embeddings (CLWEs), which learn word embeddings using information from multi- ple ...quality embeddings can be learnt even in the absence of bilingual ... See full document
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Identification, Interpretability, and Bayesian Word Embeddings
... To resolve these issues, I cast word embed- dings as a Bayesian latent variable model. Iden- tifying multidimensional latent variable models is a known problem, and I draw on solutions proposed in the ideal point ... See full document
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Delexicalized Word Embeddings for Cross lingual Dependency Parsing
... constructs word vector representations that exploit struc- tural ...each word and its contexts. These delexicalized word em- beddings, which can be trained on any set of languages and capture ... See full document
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Learning Cross lingual Word Embeddings via Matrix Co factorization
... The baseline systems are Majority class where test documents are simply classified as the class with the most training samples, and Machine translation where a phrased-based machine trans- lation system is used to ... See full document
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Multilingual and Cross-Lingual Complex Word Identification
... target word at the ...target word was annotated by 20 people, while in the test set (9,000 sentences), each target word was annotated only by a single ...a word for a single non-native speaker ... See full document
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Learning Bilingual Sentiment Specific Word Embeddings without Cross lingual Supervision
... Multilingual Word Embeddings BWE meth- ods can be extended to the case of multiple lan- guages by simply mapping all the languages to the vector space of a selected ...multilingual word embed- dings ... See full document
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Cross Lingual Dependency Parsing with Unlabeled Auxiliary Languages
... multilingual word embeddings or multi- lingual contextualized word vectors, such that the parser trained on a source language can be trans- ferred to target ...as word or- der ... See full document
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A Multi task Learning Approach to Adapting Bilingual Word Embeddings for Cross lingual Named Entity Recognition
... Cross-lingual transfer is an important technique for building natural language processing (NLP) systems for low-resource languages, where la- beled examples are scarce. The main idea is to transfer labels or ... See full document
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A robust self learning method for fully unsupervised cross lingual mappings of word embeddings
... the embeddings in one language using a least- squares objective (Mikolov et ...the embeddings in both languages to a shared space using canonical correlation analy- sis and extensions of it (Faruqui and ... See full document
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Cross lingual Models of Word Embeddings: An Empirical Comparison
... The task of cross-lingual dictionary induc- tion (Vuli´c and Moens, 2013a; Gouws et al., 2015; Mikolov et al., 2013b) judges how good cross- lingual embeddings are at detecting word pairs that ... See full document
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A Probabilistic Model for Learning Multi Prototype Word Embeddings
... distributed word representations (Morin and Bengio, 2005) (Mnih and Hinton, 2007) (Mikolov et ...one word has only one embedding, except the work of Eric Huang (Huang et ...and multi-prototype ... See full document
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Word Embeddings for Multi-label Document Classification
... pre-trained word vectors ob- tained by some semantic model ...pre-trained embeddings and fixed during the net- work training) usually bring better performance than randomly initialized and trained ... See full document
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Probabilistic FastText for Multi Sense Word Embeddings
... resulting word embeddings are highly expressive, yet straightfor- ward and interpretable, with simple, efficient, and intuitive training ...multiple word senses. In partic- ular, we represent each ... See full document
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Semi-Supervised Multi-Task Word Embeddings
... Distributed word representations have shown good perfor- mance for tasks in natural ...multiple word embeddings to increase the cover- age and accuracy of word ...eral word ... See full document
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