Tongtao Zhang


Visualizing Group Dynamics based on Multiparty Meeting Understanding
Ni Zhang | Tongtao Zhang | Indrani Bhattacharya | Heng Ji | Rich Radke
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others’ opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants’ opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.


Cross-media Event Extraction and Recommendation
Di Lu | Clare Voss | Fangbo Tao | Xiang Ren | Rachel Guan | Rostyslav Korolov | Tongtao Zhang | Dongang Wang | Hongzhi Li | Taylor Cassidy | Heng Ji | Shih-fu Chang | Jiawei Han | William Wallace | James Hendler | Mei Si | Lance Kaplan
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Building a Cross-document Event-Event Relation Corpus
Yu Hong | Tongtao Zhang | Tim O’Gorman | Sharone Horowit-Hendler | Heng Ji | Martha Palmer
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

Image-Image Search for Comparable Corpora Construction
Yu Hong | Liang Yao | Mengyi Liu | Tongtao Zhang | Wenxuan Zhou | Jianmin Yao | Heng Ji
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We present a novel method of comparable corpora construction. Unlike the traditional methods which heavily rely on linguistic features, our method only takes image similarity into consid-eration. We use an image-image search engine to obtain similar images, together with the cap-tions in source language and target language. On the basis, we utilize captions of similar imag-es to construct sentence-level bilingual corpora. Experiments on 10,371 target captions show that our method achieves a precision of 0.85 in the top search results.


Cross-document Event Coreference Resolution based on Cross-media Features
Tongtao Zhang | Hongzhi Li | Heng Ji | Shih-Fu Chang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Biography-Dependent Collaborative Entity Archiving for Slot Filling
Yu Hong | Xiaobin Wang | Yadong Chen | Jian Wang | Tongtao Zhang | Heng Ji
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Cross-media Cross-genre Information Ranking based on Multi-media Information Networks
Tongtao Zhang | Haibo Li | Hongzhao Huang | Heng Ji | Min-Hsuan Tsai | Shen-Fu Tsai | Thomas Huang
Proceedings of the Third Workshop on Vision and Language