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 Xiaohua Liu

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Average citations per article11.29
Citation Count271
Publication count24
Publication years2007-2013
Available for download14
Average downloads per article967.79
Downloads (cumulative)13,549
Downloads (12 Months)1,821
Downloads (6 Weeks)176
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24 results found Export Results: bibtexendnoteacmrefcsv

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1
July 2013 AAAI'13: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

This paper targets at automatically detecting and classifying user's suggestions from tweets. The short and informal nature of tweets, along with the imbalanced characteristics of suggestion tweets, makes the task extremely challenging. To this end, we develop a classification framework on Factorization Machines, which is effective and efficient especially in ...

2 published by ACM
February 2013 ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context: Volume 4 Issue 1, January 2013
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 6,   Downloads (12 Months): 94,   Downloads (Overall): 770

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Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel method consisting of three core elements: (1) normalization of tweets; (2) combination of a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random ...
Keywords: Semisupervised learning, model combination, tweet normalization

3
January 2013 AAAIWS'13-17: Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 1

In this paper, we address the issue of bilingual sentiment lexicon learning(BSLL) which aims to automatically and simultaneously generate sentiment words for two languages. The underlying motivation is that sentiment information from two languages can perform iterative mutual-teaching in the learning procedure. We propose to develop two classifiers to determine ...

4
January 2013 Information Processing and Management: an International Journal: Volume 49 Issue 1, January, 2013
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 2

One main challenge of Named Entities Recognition (NER) for tweets is the insufficient information in a single tweet, owing to the noisy and short nature of tweets. We propose a novel system to tackle this challenge, which leverages redundancy in tweets by conducting two-stage NER for multiple similar tweets. Particularly, ...
Keywords: Information extraction, Tweet, Named entity recognition, Two-stage labeling

5 published by ACM
August 2012 KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher: ACM
Bibliometrics:
Citation Count: 18
Downloads (6 Weeks): 10,   Downloads (12 Months): 105,   Downloads (Overall): 1,624

Full text available: PDFPDF
Microblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every minute, which both necessitates automatic opinion summarization and poses great challenges to the ...
Keywords: sentiment analysis, Twitter, opinion summarization, #hashtag, topic analysis

6
July 2012 AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Social events are events that occur between people where at least one person is aware of the other and of the event taking place. Extracting social events can play an important role in a wide range of applications, such as the construction of social network. In this paper, we introduce ...

7
July 2012 AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Tweets have become an increasingly popular source of fresh information. We investigate the task of Nominal Semantic Role Labeling (NSRL) for tweets, which aims to identify predicate-argument structures defined by nominals in tweets. Studies of this task can help fine-grained information extraction and retrieval from tweets. There are two main ...

8
July 2012 ACL '12: Proceedings of the ACL 2012 System Demonstrations
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 6,   Downloads (12 Months): 16,   Downloads (Overall): 110

Full text available: PDFPDF
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 ...

9
July 2012 ACL '12: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 7,   Downloads (12 Months): 74,   Downloads (Overall): 418

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Tweets represent a critical source of fresh information, in which named entities occur frequently with rich variations. We study the problem of named entity normalization (NEN) for tweets. Two main challenges are the errors propagated from named entity recognition (NER) and the dearth of information in a single tweet. We ...

10 published by ACM
October 2011 CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management
Publisher: ACM
Bibliometrics:
Citation Count: 44
Downloads (6 Weeks): 32,   Downloads (12 Months): 451,   Downloads (Overall): 4,389

Full text available: PDFPDF
Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various interesting topics, such as brands, products or celebrities, etc. Naturally, people ...
Keywords: graph-based classification, hashtag, sentiment analysis, twitter

11
August 2011 AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 1

Semantic Role Labeling (SRL) for tweets is a meaningful task that can benefit a wide range of applications such as finegrained information extraction and retrieval from tweets. One main challenge of the task is the lack of annotated tweets, which is required to train a statistical model. We introduce self-training ...

12 published by ACM
July 2011 SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 19,   Downloads (Overall): 367

Full text available: PDFPDF
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 ...
Keywords: tweet search, information extraction

13
July 2011 IJCAI'11: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Publisher: AAAI Press
Bibliometrics:
Citation Count: 8

As tweets have become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its central role in a wide range of tweet related studies such as fine-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is ...

14
June 2011 HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 6,   Downloads (Overall): 236

Full text available: PDFPDF
This paper presents Engkoo , a system for exploring and learning language. It is built primarily by mining translation knowledge from billions of web pages - using the Internet to catch language in motion. Currently Engkoo is built for Chinese users who are learning English; however the technology itself is ...

15
June 2011 HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 63
Downloads (6 Weeks): 49,   Downloads (12 Months): 372,   Downloads (Overall): 1,756

Full text available: PDFPDF
The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The ...

16
June 2011 HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 89
Downloads (6 Weeks): 59,   Downloads (12 Months): 638,   Downloads (Overall): 3,025

Full text available: PDFPDF
Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here ...

17
February 2011 CICLing'11: Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

We study the task of correcting verb selection errors for English as a Second Language (ESL) learners, which is meaningful but also challenging. The difficulties of this task lie in two aspects: the lack of annotated data and the diversity of verb usage context. We propose a perceptron based novel ...
Keywords: ESL, verb selection, perceptron learning

18
December 2010 IALP '10: Proceedings of the 2010 International Conference on Asian Language Processing
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 1

We raise the problem of evaluating the quality of bilingual sentences mined from the web, which is critical for such applications as statistical machine translation (SMT) and English as Second Language (ESL) learning. To tackle this problem, we propose a novel method that integrates multiple linguistic features related to spelling, ...
Keywords: linguistic quality evaluation, bilingual sentence pairs, classification

19
October 2010 EMNLP '10: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 0,   Downloads (12 Months): 7,   Downloads (Overall): 128

Full text available: PDFPDF
In this paper we develop an approach to tackle the problem of verb selection for learners of English as a second language (ESL) by using features from the output of Semantic Role Labeling (SRL). Unlike existing approaches to verb selection that use local features such as n-grams, our approach exploits ...

20
August 2010 COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 1,   Downloads (12 Months): 14,   Downloads (Overall): 76

Full text available: PDFPDF
We propose a novel MLN-based method that collectively conducts SRL on groups of news sentences. Our method is built upon a baseline SRL, which uses no parsers and leverages redundancy. We evaluate our method on a manually labeled news corpus and demonstrate that news redundancy significantly improves the performance of ...



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