Random Walks for Knowledge-Based Word Sense Disambiguation
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Information RetrievalMachine TranslationQuestion AnsweringWord Sense DisambiguationSimilar Papers 제목 키워드 기반
Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks
Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow…
Word Sense DisambiguationIntegrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introd…
ClusteringMachine TranslationNMTTranslation+1Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs
This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space …
General ClassificationSentenceWord EmbeddingsUnsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpret…
Word Sense DisambiguationLeveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguat…
Word Sense Disambiguation