Aixin Sun
2020
Span-based Localizing Network for Natural Language Video Localization
Hao Zhang
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Aixin Sun
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Wei Jing
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Joey Tianyi Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods ; and adopting span-based QA framework is a promising direction to solve NLVL.
2019
Robust Representation Learning of Biomedical Names
Minh C. Phan
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Aixin Sun
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Yi Tay
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.
2017
Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules
Xiaoshi Zhong
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Aixin Sun
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Erik Cambria
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and find that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment on benchmark datasets and tweets data shows that SynTime outperforms state-of-the-art methods.
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Co-authors
- Hao Zhang 1
- Wei Jing 1
- Joey Tianyi Zhou 1
- Xiaoshi Zhong 1
- Erik Cambria 1
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Venues
- ACL3