![]() ![]() Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4. Observed during training on both generic and domain-specific datasets: Experimental resultsĭemonstrate that the proposed model is able to detect synonym sets that are not Pieces of contexts in which the entity is mentioned, and compares theĬontext-level similarity via a bilateral matching schema. Instead of using entities features, SYNONYMNET makes use of multiple In the end, it is about how the context does not change and is easily comprehended by the reader. SYNONYMNET to determine whether or not two given entities are synonym with each Synonyms-words that denote the same concept and are interchangeable in many contexts-are grouped into unordered sets (synsets). Of the key components in synonym discovery, we introduce a neural network model Paper, we generalize the distributional hypothesis to a multi-context settingĪnd propose a synonym discovery framework that detects entity synonyms fromįree-text corpora with considerations on effectiveness and robustness. To leverage diverse contexts where entities are mentioned, in this In contrast, a bidirectional language model could also gain context from with and you, which might help the model generate. On structured annotations from a single piece of context where the entity is ![]() Existing works either only utilize entity features, or rely Abstract: This paper presents a context-based synonym database that could aid human or machine to find the appropriate synonyms of words based on their use. Synonyms A synonym is a word or phrase that means the same or is very similar to another word. Setting benefits various tasks such as entity disambiguation or knowledge graphĬanonicalization. Context clues are words or phrases in the sentence or paragraph that help the reader to figure out the meaning of the unknown word. Download a PDF of the paper titled Entity Synonym Discovery via Multipiece Bilateral Context Matching, by Chenwei Zhang and 4 other authors Download PDF Abstract: Being able to automatically discover synonymous entities in an open-world
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