![]() ![]() Therefore, subsequent studies were devoted to automatically acquiring synonymous lexical patterns from corpora. However, manually predefining the synonymous lexical patterns is time-consuming and laborious. For example, English strings \(\\) is a synonym set. An entity synonym set usually contains several different strings representing an identical entity. Mining entity synonym set is an important task for many entity-based downstream applications, such as knowledge graph construction, taxonomy learning, and question answering. Experimental results on two Chinese real-world datasets demonstrate that the proposed approach is effective and outperforms the selected existing state-of-the-art approaches to the Chinese entity synonym set expansion task. The filtering strategy enhances semantic and domain consistencies to filter out wrong synonym entities, thereby mitigating error propagation. Second, a filtering-strategy-based set expansion algorithm is presented to generate Chinese entity synonym sets. The classifier tracks the holistic semantics of bilateral contexts and is capable of imposing soft holistic semantic constraints to improve synonym prediction. First, a bilateral-context-based Siamese network classifier is proposed to determine whether a new entity should be inserted into the existing entity synonym set. Specifically, the approach consists of two novel components. This paper introduces an approach for expanding Chinese entity synonym sets based on bilateral context and filtering strategy. Due to the flexibility and complexity of the Chinese language expression, the aforementioned approaches are still difficult to expand entity synonym sets robustly from Chinese text, because these approaches fail to track holistic semantics among entities and suffer from error propagation. Previous approaches use linguistic syntax, distributional, and semantic features to expand entity synonym sets from text corpora. Entity synonyms play a significant role in entity-based tasks.
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