ABSTRACT
The task of temporal slot filling (TSF) is to extract the values (or called facts) of specific attributes for a given entity from text data and find the time points when the values were valid. It is challenging to find precise time points with incomplete and noisy temporal contexts in the text. In this work, we propose an unsupervised approach of two modules that mutually enhance each other: one is a reliability estimator on fact extractors conditionally to the temporal contexts; the other is a fact trustworthiness estimator based on the extractor's reliability. The iterative learning process reduces the noise of the extractions. Experiments demonstrate that our approach, with the novel design, can accurately and efficiently extract precise temporal facts from newspaper corpora.
- Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D Manning. 2015. Leveraging linguistic structure for open domain information extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Vol. 1. 344-354.Google ScholarCross Ref
- Michele Banko, Michael J Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni. 2007. Open information extraction from the web.. In IJCAI, Vol. 7. 2670-2676. Google ScholarDigital Library
- Laure Berti-Equille. 2015. Data veracity estimation with ensembling truth discovery methods. In Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 2628-2636. Google ScholarDigital Library
- Melisachew Wudage Chekol. 2017. Scaling probabilistic temporal query evaluation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 697-706. Google ScholarDigital Library
- Aron Culotta and Jeffrey Sorensen. 2004. Dependency tree kernels for relation extraction. In Proceedings of the 42nd annual meeting on association for computational linguistics. Association for Computational Linguistics, 423. Google ScholarDigital Library
- Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, and Guergana Savova. 2017. Neural temporal relation extraction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Vol. 2. 746-751.Google ScholarCross Ref
- Oren Etzioni, Anthony Fader, Janara Christensen, Stephen Soderland, and Mausam Mausam. 2011. Open information extraction: The second generation.. In IJCAI, Vol. 11. 3-10. Google ScholarDigital Library
- Katrin Fundel, Robert Küffner, and Ralf Zimmer. 2006. RelEx-Relation extraction using dependency parse trees. Bioinformatics 23, 3 (2006), 365-371. Google ScholarDigital Library
- Alban Galland, Serge Abiteboul, Ame´lie Marian, and Pierre Senellart. 2010. Corroborating information from disagreeing views. In Proceedings of the third ACM international conference on Web search and data mining. ACM, 131-140. Google ScholarDigital Library
- Kiril Gashteovski, Rainer Gemulla, and Luciano Del Corro. 2017. Minie: minimizing facts in open information extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2630-2640.Google ScholarCross Ref
- Sally A Goldman and Manfred K Warmuth. 1995. Learning binary relations using weighted majority voting. Machine Learning 20, 3 (1995), 245-271. Google ScholarDigital Library
- Rahul Gupta, Alon Halevy, Xuezhi Wang, Steven Euijong Whang, and Fei Wu. 2014. Biperpedia: An ontology for search applications. Proceedings of the VLDB Endowment 7, 7 (2014), 505-516. Google ScholarDigital Library
- Alon Halevy, Natalya Noy, Sunita Sarawagi, Steven Euijong Whang, and Xiao Yu. 2016. Discovering structure in the universe of attribute names. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 939-949. Google ScholarDigital Library
- Julia Hirschberg and Christopher D Manning. 2015. Advances in natural language processing. Science 349, 6245 (2015), 261-266.Google Scholar
- Tuan-Anh Hoang-Vu, Huy T Vo, and Juliana Freire. 2016. A unified index for spatio-temporal keyword queries. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 135-144. Google ScholarDigital Library
- Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M Kaplan, Timothy P Hanratty, and Jiawei Han. 2017. Metapad: Meta pattern discovery from massive text corpora. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 877-886. Google ScholarDigital Library
- Qi Li, Meng Jiang, Xikun Zhang, Meng Qu, Timothy P Hanratty, Jing Gao, and Jiawei Han. 2018. Truepie: Discovering reliable patterns in pattern-based information extraction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1675-1684. Google ScholarDigital Library
- Qi Li, Yaliang Li, Jing Gao, Bo Zhao, Wei Fan, and Jiawei Han. 2014. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 1187-1198. Google ScholarDigital Library
- Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. 2016. A survey on truth discovery. ACM Sigkdd Explorations Newsletter 17, 2 (2016), 1-16. Google ScholarDigital Library
- Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2017. Representations of time expressions for temporal relation extraction with convolutional neural networks. BioNLP 2017 (2017), 322-327.Google Scholar
- Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 1003-1011. Google ScholarDigital Library
- Ndapandula Nakashole, Gerhard Weikum, and Fabian Suchanek. 2012. PATTY: a taxonomy of relational patterns with semantic types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 1135-1145. Google ScholarDigital Library
- Nils Reimers, Nazanin Dehghani, and Iryna Gurevych. 2016. Temporal anchoring of events for the timebank corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 2195-2204.Google ScholarCross Ref
- Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin. 2013. Relation extraction with matrix factorization and universal schemas. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 74-84.Google Scholar
- Michael Schmitz, Robert Bart, Stephen Soderland, Oren Etzioni, and others. 2012. Open language learning for information extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 523-534. Google ScholarDigital Library
- Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, and Jiawei Han. 2018. Automated phrase mining from massive text corpora. IEEE Transactions on Knowledge and Data Engineering 30, 10(2018), 1825-1837.Google ScholarCross Ref
- Avirup Sil and Silviu-Petru Cucerzan. 2014. Towards Temporal Scoping of Relational Facts based on Wikipedia Data. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning. 109-118.Google ScholarCross Ref
- Alejandro Sobrino, Cristina Puente, and Jose´ Ángel Olivas. 2017. Mining Temporal Causal Relations in Medical Texts. In International Joint Conference SOCO'17-CISIS'17-ICEUTE'17 León, Spain, September 6-8, 2017, Proceeding. Springer, 449-460.Google Scholar
- Jannik Strötgen and Michael Gertz. 2015. A baseline temporal tagger for all languages. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 541-547.Google ScholarCross Ref
- David Tsurel, Dan Pelleg, Ido Guy, and Dafna Shahaf. 2017. Fun Facts: Automatic Trivia Fact Extraction from Wikipedia. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 345-354. Google ScholarDigital Library
- Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, and Aidong Zhang. 2016. Towards confidence in the truth: A bootstrapping based truth discovery approach. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1935-1944. Google ScholarDigital Library
- Houping Xiao, Yaliang Li, Jing Gao, Fei Wang, Liang Ge, Wei Fan, Long H Vu, and Deepak S Turaga. 2015. Believe it today or tomorrow? detecting untrustworthy information from dynamic multi-source data. In Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 397-405.Google ScholarCross Ref
- Mohamed Yahya, Steven Whang, Rahul Gupta, and Alon Halevy. 2014. Renoun: Fact extraction for nominal attributes. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 325-335.Google ScholarCross Ref
- Xiaoxin Yin, Jiawei Han, and S Yu Philip. 2008. Truth discovery with multiple conflicting information providers on the web. IEEE Transactions on Knowledge and Data Engineering 20, 6(2008), 796-808. Google ScholarDigital Library
- Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, and Jiawei Han. 2018. TaxoGen: Constructing Topical Concept Taxonomy by Adaptive Term Embedding and Clustering. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.Google ScholarDigital Library
Recommendations
Extracting Events and Temporal Expressions from Text
ICSC '10: Proceedings of the 2010 IEEE Fourth International Conference on Semantic ComputingExtracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal ...
A Graphical Representation for Uncertain and Incomplete Temporal Knowledge
GCIS '10: Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems - Volume 01Complete and Absolute temporal knowledge is usually not always available for many knowledge based systems, notably in the domain of Artificial Intelligence. Based on a time theory that takes both points and intervals as primitive, this paper introduces ...
Extracting meaning from temporal nouns and temporal prepositions
Special Issue on Temporal Information ProcessingThis article provides a compositional semantics for temporal nouns and temporal prepositions that are annotated as temporal prepositional phrases or noun phrases by an automatic tagging system (e.g., last Monday, on Dec. 1st, for three weeks or before ...
Comments