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A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches
This paper presents and compares WordNet-based and distributional similarity approaches. The strengths and weaknesses of each approach regarding similarity and relatedness tasks are discussed, and aExpand
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Personalizing PageRank for Word Sense Disambiguation
In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph ofExpand
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A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation hasExpand
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Unsupervised Neural Machine Translation
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have beenExpand
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SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answerExpand
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SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity
Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000Expand
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Learning bilingual word embeddings with (almost) no bilingual data
Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax thisExpand
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Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Mapping word embeddings of different languages into a single space has multiple applications. In order to map from a source space into a target space, a common approach is to learn a linear mappingExpand
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Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations
Using a dictionary to map independently trained word embeddings to a shared space has shown to be an effective approach to learn bilingual word embeddings. In this work, we propose a multi-stepExpand
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*SEM 2013 shared task: Semantic Textual Similarity
In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core taskExpand
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