acl acl2012 acl2012-167 acl2012-167-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Xiaohua Liu ; Furu Wei ; Ming Zhou ; QuickView Team Microsoft
Abstract: Tweets have become a comprehensive repository for real-time information. However, it is often hard for users to quickly get information they are interested in from tweets, owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView, an NLP-based tweet search platform to tackle this issue. Specifically, it exploits a series of natural language processing technologies, such as tweet normalization, named entity recognition, semantic role labeling, sentiment analysis, tweet classification, to extract useful information, i.e., named entities, events, opinions, etc., from a large volume of tweets. Then, non-noisy tweets, together with the mined information, are indexed, on top of which two brand new scenarios are enabled, i.e., categorized browsing and advanced search, allowing users to effectively access either the tweets or fine-grained information they are interested in.
Jenny Rose Finkel and Christopher D. Manning. 2009. Nested named entity recognition. In EMNLP, pages 141–150. Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In ACL, pages 363–370. Martin Jansche and Steven P. Abney. 2002. Information extraction from voicemail transcripts. In EMNLP, pages 320–327. Long Jiang, Mo Yu, Ming Zhou, and Xiaohua Liu. 2011. Target-dependent twitter sentiment classification. In ACL. Peter Koomen, Vasin Punyakanok, Dan Roth, and Wentau Yih. 2005. Generalized inference with multiple semantic role labeling systems. In CONLL, pages 181–184. Vijay Krishnan and Christopher D. Manning. 2006. An effective two-stage model for exploiting non-local de- pendencies in named entity recognition. InACL, pages 1121–1 128. George R. Krupka and Kevin Hausman. 1998. Isoquest: Description of the netowlTM extractor system as used in muc-7. In MUC-7. Xiaohua Liu, Kuan Li, Bo Han, Ming Zhou, Long Jiang, Zhongyang Xiong, and Changning Huang. 2010. Semantic role labeling for news tweets. In Coling, pages 698–706. Llu ı´s M `arquez, Pere Comas, Jes u´s Gim e´nez, and Neus Catal `a. 2005. Semantic role labeling as sequential tagging. In CONLL, pages 193–196. Ivan Meza-Ruiz and Sebastian Riedel. 2009. Jointly identifying predicates, arguments and senses using markov logic. In NAACL, pages 155–163. Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In CoNLL, pages 147–155. Benjamin Rozenfeld and Ronen Feldman. 2008. Selfsupervised relation extraction from the web. Knowl. Inf. Syst., 17: 17–33, October. Roser Saur ı´, Robert Knippen, Marc Verhagen, and James Pustejovsky. 2005. Evita: A robust event recognizer for qa systems. In EMNLP, pages 700–707. Sameer Singh, Dustin Hillard, and Chris Leggetter. 2010. Minimally-supervised extraction of entities from text advertisements. In HLT-NAACL, pages 73–81 . Andreas Stolcke. 2002. SRILM an extensible language modeling toolkit. In ICSLP, volume 2, pages 901–904. Nianwen Xue. 2004. Calibrating features for semantic role labeling. In In Proceedings of EMNLP 2004, pages 88–94. –