S yn ET : Synonym Expansion using Transitivity (2020)

Abstract: In this paper, we study a new task of synonym expansion using transitivity, and propose a novel approach named SynET, which considers both the contexts of two given synonym pairs. It introduces an auxiliary task to reduce the impact of noisy sentences, and proposes a Multi-Perspective Entity Matching Network to match entities from multiple perspectives. Extensive experiments on a real-world dataset show the effectiveness of our approach.

## introduction synonym discovery has become an important task, which can benefit many downstream applications, such as web search @xcite , question answering @xcite , knowledge graph construction @xcite , clinical text analysis @xcite , and etc. one straightforward approach to obtain synonyms is from public knowledge bases, such as wordnet @xcite and dbpedia @xcite . for example, wordnet groups terms into synsets, and dbpedia uses redirects to uris to indicate synonyms. however, these synonyms are constructed manually, which makes the coverage rather limited. two types of approaches are widely exploited to discover synonyms automatically from text corpora, including the distributional based approaches @xcite and the pattern based approaches @xcite . the distributional based approaches assume that if two terms appear in similar contexts, they are likely to be synonyms. for example, "usa" and "the united states" are often mentioned in similar contexts, so they both refer to the same country. the pattern based approaches lay emphasis on the local * corresponding author contexts, such as "commonly known as". however, they both have some limitations. the distributional based approaches suffer from low precision, while the pattern based approaches suffer from low recall. in order to address these limitations, dpe @xcite integrated these two approaches for synonym discovery. intuitively, people believe that synonyms possess transitivity, that is (m i , synonym, m b ) ∧ (m b , synonym, m j )→(m i , synonym, m j ), where m i , m b and m j are three different mentions, and m b is the bridge mention of two synonym pairs (m i , m b ) and (m b , m j ). this transitivity can be used for synonym discovery directly from existing synonyms. for example, the united states of america and the united states are synonyms, the united states and u.s. are synonyms, so the united states of america and u.s. should also be synonyms. distiller @xcite even designed loss functions based on the synonym transitivity properties. baidu baike foot_0 and wikidata foot_1 both use "also known as" to indicate synonyms, as shown in figure 1 and 木莲 (hibiscus). therefore, using transitivity between synonym pairs directly would make wrong synonym pairs. therefore, it is hazardous to infer (m i , synonym, m j ) directly, when (m i , synonym, m b ) and (m b , synonym, m j ) are given. there are several challenges to address this problem. firstly, if we directly use distributional approaches to predict whether [canary] [prunus persica] for the task, we propose a novel framework, named synet, which leverages both the contexts of two given synonym pairs. first, it introduces an auxiliary task to reduce the impact of noisy sentences further, and then proposes a multi-perspective mention matching network (mpm-m) to match mentions from multiple perspectives, including m2m (mention-to-mention), m2b (mention-to-mention bag) and b2b (mention bagto-mention bag) matches. our contributions in this paper are as follows: id sentence 1 (jinsique[canary] is also called furong[canary], baiyu[canary], baiyan[canary], and is one of the passerine birds. 2 (jinsique[canary] is also called furong[canary], baiyan[canary] and yuniao[canary]. 3 … … id sentence 1 , (this flower is as gorgeous as lotus, so it is named as furong[hibiscus] and mulian[hibiscus]) 2 (furong[hibiscus] is also also known as jushanghua[hibiscus], mulian[hibiscus], and etc. 3 … … synet (our framework) ## task definition we first introduce basic concepts and their notations, and then present the task definition. ## the synet approach in this section, we introduce our proposed approach synet for synonym discovery using transitivity. as shown in figure 4 , our proposed synet approach mainly consists of three components, including sentence encoder (section 3.1), mention encoder (section 3.3) and multi-perspective mention matching network (mpmm) (section 3.4). in the following sections, we will elaborate each component in detail. ## related work synonym discovery is a crucial task in nlp, and many efforts have been invested. one straightforward approach to obtain synonyms is from pub- @xcite , conceptnet @xcite and dbpedia @xcite . however, these synonyms are constructed manually, which makes the coverage rather limited. many efforts have been made to discover synonyms automatically. some approaches discover synonyms from query logs @xcite @xcite and web table schemas @xcite . however, these approaches are limited to structured or semistructured data. recently, researchers focus on mining synonyms from a raw text corpus, which is more challenging. two types of approaches are widely exploited, including the pattern based approaches @xcite and the distributional based approaches @xcite @xcite . the pattern based approaches lay emphasis on the local contexts, such as "commonly known as". while the distributional based approaches assume that if two terms appear in similar contexts, they are likely to be synonyms. for example, synonymnet @xcite proposed a multi-context bilateral matching framework for synonym discovery from free-text corpus. surf-con @xcite ) discovered synonyms on privacy-aware clinical data by utilizing the surface form information and the global context information. however, they suffer from either low precision or low recall. thus, dpe @xcite and synmine @xcite integrated these two approaches for synonym discovery. moreover, synsetmine @xcite learned a set-instance classifier to generate entity synonym sets from a given vocabulary using example sets from external knowledge bases as distant supervision. our approach focuses on mining synonyms using transitivity which is not the focus of the previous works. although @xcite also utilized transitivity, they assumed that transitivity does hold in almost all cases for attribute synonyms, so they used this transitivity property to discover clusterbased synonyms by a linear programming-based algorithm. while our approach called this property into question, and only used it to generate synonym candidates. ## conclusion in this paper, we study a new task of synonym expansion using transitivity, and propose a novel approach named synet. to the best of our knowledge, it is the first time to study this problem. the synet considers both the contexts of two given synonym pairs. it introduce an auxiliary task to reduce the impact of noisy sentences, and proposes a multi-perspective entity matching network to match entities from multiple perspectives. extensive experiments on a real-world dataset show the effectiveness of our approach.