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Yifang Sun

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6 papers

1 author row

AAAI Conference 2021 Conference Paper

Improving the Efficiency and Effectiveness for BERT-based Entity Resolution

Bing Li
Yukai Miao
Yaoshu Wang
Yifang Sun
Wei Wang

BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed to fine-tuning pretrained language models and the deep pair-wise interaction. Albeit being remarkably effective, it comes with a steep increase in computational cost, as the deep-interaction requires to exhaustively compute every tuple pair to search for coreferences. For ER task, it is often prohibitively expensive due to the large cardinality to be matched. To tackle this, we introduce a siamese network structure that independently encodes tuples using BERT but delays the pair-wise interaction via an enhanced alignment network. This siamese structure enables a dedicated blocking module to quickly filter out obviously dissimilar tuple pairs, and thus drastically reduces the cardinality of fine-grained matching. Further, the blocking and entity matching are integrated into a multi-task learning framework for facilitating both tasks. Extensive experiments on multiple datasets demonstrate that our model significantly outperforms state-of-the-art models (including BERT) in both efficiency and effectiveness.

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AAAI Conference 2020 Conference Paper

Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Muhammad Asif Ali
Yifang Sun
Bing Li
Wei Wang

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention’s sentence-speciﬁc context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that reﬁnes the noisy mention representations by attending over corpus-level contextual clues prior to the end classiﬁcation. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10. 2% and 8. 3% for macro-f1 and micro-f1 respectively.

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AAAI Conference 2020 Conference Paper

GraphER: Token-Centric Entity Resolution with Graph Convolutional Neural Networks

Bing Li
Wei Wang
Yifang Sun
Linhan Zhang
Muhammad Asif Ali
Yi Wang

Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by comparing similarities of pre-aligned attributes, which require the schemas of records to be identical and are too coarse-grained to capture subtle key information within a single attribute. In this paper, we propose a novel graph-based ER model GraphER. Our model is token-centric: the ﬁnal matching results are generated by directly aggregating token-level comparison features, in which both the semantic and structural information has been softly embedded into token embeddings by training an Entity Record Graph Convolutional Network (ER-GCN). To the best of our knowledge, our work is the ﬁrst effort to do token-centric entity resolution with the help of GCN in entity resolution task. Extensive experiments on two real-world datasets demonstrate that our model stably outperforms state-of-the-art models.

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AAAI Conference 2020 Conference Paper

HAMNER: Headword Amplified Multi-Span Distantly Supervised Method for Domain Specific Named Entity Recognition

Shifeng Liu
Yifang Sun
Bing Li
Wei Wang
Xiang Zhao

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufﬁcient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in speciﬁc domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classiﬁes all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.

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AAAI Conference 2020 Conference Paper

Recursively Binary Modification Model for Nested Named Entity Recognition

Bing Li
Shifeng Liu
Yifang Sun
Wei Wang
Xiang Zhao

Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modiﬁcation relations between subentity types. In this paper, we present a novel Recursively Binary Modiﬁcation model for nested named entity recognition. Our model utilizes the modiﬁcation relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the ﬁrst effort that uses modiﬁcation relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in ﬂat NER task, without relying on any extra annotations or NLP tools.

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AAAI Conference 2019 Conference Paper

Antonym-Synonym Classification Based on New Sub-Space Embeddings

Muhammad Asif Ali
Yifang Sun
Xiaoling Zhou
Wei Wang
Xiang Zhao

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.

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