Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction

Fei, Hongliang; Tan, Shulong; Li, Ping

doi:10.1145/3292500.3330914

Cited by 21 publications

(22 citation statements)

References 20 publications

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“…Our work, distinguishing with them, aims to discover entity synonyms from raw text corpora, which is more practical and challenging for real-world application. Ranking-based and classification-based approaches were proposed to distinguish between candidate entities and the query entity by representing entities with different features, such as co-occurrence statistics [8], textual pattern [6], distributional similarity [9], and semantic types of entities [10], [22]. Recently, Qu et al [7] proposed integrating distributional features and textual patterns to discover the entity synonym with knowledge bases automatically.…”

Section: Entity Synonym Discoverymentioning

confidence: 99%

Set-aware Entity Synonym Discovery with Flexible Receptive Fields

Pei¹,

Yu²,

Zhang³

2021

IEEE Trans. Knowl. Data Eng.

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Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications, e.g., web search and question answering. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by making a breakthrough in using entity synonym set information. The contextual information of entities and entity synonym sets are arranged by a two-level network from which entities and entity synonym sets can be mapped into the same embedding space to facilitate ESD by encoding the high-order contexts from flexible receptive fields. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.

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Section: Entity Synonym Discoverymentioning

confidence: 99%

Set-aware Entity Synonym Discovery with Flexible Receptive Fields

Pei¹,

Yu²,

Zhang³

2021

IEEE Trans. Knowl. Data Eng.

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“…Given a pair of entities, our synonym discovery model returns the probability that they are synonymous. We use two types of features for entity pairs 3 : (1) lexical features based on entity surface names (e.g., Jaro-Winkler similarity , token edit distance (Fei et al, 2019), etc), and (2) semantic features based on entity embeddings (e.g., cosine similarity between two entities' SkipGram embeddings). As these feature values have different scales, we use a tree-based boosting model XGBoost (Chen and Guestrin, 2016) to predict whether two entities are synonyms.…”

Section: Proposed Synonym Discovery Modelmentioning

confidence: 99%

“…In comparison, this work aims to develop a method to extract synonym sets directly from raw text corpus. Given a corpus and a term list, one can leverage surface string , co-occurrence statistics (Baroni and Bisi, 2004), textual pattern (Yahya et al, 2014), distributional similarity (Wang et al, 2015), or their combinations (Qu et al, 2017;Fei et al, 2019) to extract synonyms. These methods mostly find synonymous term pairs or a rank list of query entity's synonym, instead of entity synonym sets.…”

Section: Related Workmentioning

confidence: 99%

SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery

Shen¹,

Qiu²,

Shang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependences. In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have similar likelihoods of belonging to various semantic classes. This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other. SynSetExpan uses a synonym discovery model to include popular entities' infrequent synonyms into the set, which boosts the set expansion recall. Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy.To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing. Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks. * Equal Contributions.

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“…One straightforward approach to obtain synonyms is from pub- 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 (Nguyen et al, 2017) and the distributional based approaches (Wang et al, 2019a,b;Fei et al, 2019;Zhang et al, 2019). The pattern based approaches lay emphasis on the local contexts, such as "commonly known as".…”

Section: Related Workmentioning

confidence: 99%

“…Two types of approaches are widely exploited to discover synonyms automatically from text corpora, including the distributional based approaches (Wang et al, 2019a,b;Fei et al, 2019) and the pattern based approaches (Nguyen et al, 2017). The distributional based approaches assume that if two terms appear in similar contexts, they are likely to be synonyms.…”

Section: Introductionmentioning

confidence: 99%

SynET: Synonym Expansion using Transitivity

Shen

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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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 realworld dataset show the effectiveness of our approach.

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Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction

Cited by 21 publications

References 20 publications

Set-aware Entity Synonym Discovery with Flexible Receptive Fields

Set-aware Entity Synonym Discovery with Flexible Receptive Fields

SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery

SynET: Synonym Expansion using Transitivity

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