HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity
In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask.
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Tasks
Cross-Lingual Word EmbeddingsMachine TranslationMultilingual Word EmbeddingsSemantic SimilaritySemantic Textual SimilaritySentenceStock Price PredictionTask 2Text ClassificationTranslationTransliterationWord EmbeddingsWord SimilaritySimilar Papers 제목 키워드 기반
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