Aoying Zhou


2019

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SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Chengyu Wang | Xiaofeng He | Aoying Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.

2017

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Transductive Non-linear Learning for Chinese Hypernym Prediction
Chengyu Wang | Junchi Yan | Aoying Zhou | Xiaofeng He
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.

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A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
Chengyu Wang | Xiaofeng He | Aoying Zhou
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for long-tailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.

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Learning Fine-grained Relations from Chinese User Generated Categories
Chengyu Wang | Yan Fan | Xiaofeng He | Aoying Zhou
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.

2011

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Query Weighting for Ranking Model Adaptation
Peng Cai | Wei Gao | Aoying Zhou | Kam-Fai Wong
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies