Currently browsing: Items authored or edited by Yulan He

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Burel, Gregoire; Mulholland, Paul; He, Yulan and Alani, Harith (2015). Predicting Answering Behaviour in Online Question Answering Communities. In: HT '15 Proceedings of the 26th ACM Conference on Hypertext & Social Media, ACM, pp. 201–210.

Burel, Gregoire; He, Yulan; Mulholland, Paul and Alani, Harith (2015). Modelling Question Selection Behaviour in Online Communities. In: WWW '15 Companion, ACM, pp. 357–358.

Burel, Gregoire and He, Yulan (2014). Quantising contribution effort in online communities. In: WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web, pp. 549–550.

Burel, Gregoire and He, Yulan (2013). A question of complexity - measuring the maturity of online enquiry communities. In: 24th ACM Conference on Hypertext and Social Media, 1-3 May 2013, Paris, France.

Burel, Gregoire; He, Yulan and Alani, Harith (2012). Automatic identification of best answers in online enquiry communities. In: 9th Extended Semantic Web Conference, 27-31 May 2012, Crete.

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Cano, Amparo Elizabeth; He, Yulan and Alani, Harith (2014). Stretching the life of Twitter classifiers with time-stamped semantic graphs. In: The Semantic Web – ISWC 2014: Proceedings, Part II, Springer International Publishing, pp. 341–357.

Cano, A. Elisabeth; He, Yulan and Alani, Harith (2014). The topics they are a-changing - characterising topics with time-stamped semantic graphs. In: Proceedings of the ISWC 2014 Posters & Demonstrations Track, CEUR, pp. 409–412.

Cano Basave, Amparo Elizabeth; He, Yulan and Xu, Ruifeng (2014). Automatic labelling of topic models learned from Twitter by summarisation. In: The 52nd Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference: Volume 2: Short Papers, Association for Computational Linguistics (ACL), pp. 618–624.

Cano Basave, Amparo Elizabeth; He, Yulan; Liu, Kang and Zhao, Jun (2013). A weakly supervised Bayesian model for violence detection in social media. In: Sixth International Joint Conference on Natural Language Processing: Proceedings of the Main Conference, Asian Federation of Natural Language Processing, pp. 109–117.

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Dong, Haichao; Hui, Siu Cheung and He, Yulan (2006). Structural analysis of chat messages for topic detection. Online Information Review, 30(5) pp. 496–516.

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Ferguson, Rebecca; Wei, Zhongyu; He, Yulan and Buckingham Shum, Simon (2013). An evaluation of learning analytics to identify exploratory dialogue in online discussions. In: Third Conference on Learning Analytics and Knowledge (LAK 2013), 8-12 Apr 2013, Leuven, Belgium, ACM, pp. 85–93.

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He, Yulan; Lin, Chenghua; Gao, Wei and Wong, Kam-Fai (2017). Tracking Sentiment and Topic Dynamics from Social Media. In: Wong, Kam-Fai; Gao, Wei; Xu, Ruifeng and Li, Wenjie eds. Social Media Content Analysis Natural Language Processing and Beyond. Series on Language Processing, Pattern Recognition, and Intelligent Systems:, 5. World Scientific, pp. 457–465.

Huynh, Trung; He, Yulan and Rüger, Stefan (2015). Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis. In: Advances in Information Retrieval (Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas and Fuhr, Norbert eds.), Lecture Notes in Computer Science, Springer International Publishing, pp. 447–452.

He, Yulan; Lin, Chenghua; Gao, Wei and Wong, Kam-Fai (2013). Dynamic joint sentiment-topic model. ACM Transactions on Intelligent Systems and Technology, 5(1), article no. 6.

He, Yulan (2012). Incorporating sentiment prior knowledge for weakly-supervised sentiment analysis. ACM Transactions on Asian Language Information Processing, 11(2), article no. 4.

He, Yulan; Lin, Chenghua and Cano Basave, Amparo (2012). Online sentiment and topic dynamics tracking over the streaming data. In: 2012 ASE International Conference on Social Computing (SocialCom 2012), 03-05 Sep 2012, Amsterdam, The Netherlands.

He, Yulan; Saif, Hassan; Wei, Zhongyu and Wong, Kam-Fai (2012). Quantising opinions for political tweets analysis. In: LREC 2012, Eighth International Conference on Language Resources and Evaluation, 21-27 May 2012, Istanbul, Turkey.

He, Yulan and Zhou, Deyu (2011). Self-training from labelled features for sentiment analysis. Information Processing & Management, 47(4) pp. 606–616.

He, Yulan (2011). Latent sentiment model for weakly-supervised cross-lingual sentiment classification. In: 33RD European Conference on Information Retrieval (ECIR 2011), 18-21 Apr 2011, Dublin, Ireland.

He, Yulan; Lin, Chenghua and Alani, Harith (2011). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 19-24 Jun 2011, Portland, Oregon, USA.

He, Yulan (2010). Learning sentiment classification model from labeled features. In: The 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), 26-30 Oct 2010, Toronto, Canada.

He, Yulan (2010). Goal detection from natural language queries. In: 15th International Conference on Applications of Natural Language to Information Systems (NLDB 2010), 23-25 Jun 2010, Cardiff, UK.

He, Yulan; Alani, Harith and Zhou, Deyu (2010). Exploring English lexicon knowledge for Chinese sentiment analysis. In: CIPS-SIGHAN Joint Conference on Chinese Language Processing, 28-29 Aug 2010, Beijing, China.

He, Yulan; Hui, Siu Cheung and Quan, Tho Thanh (2009). Automatic summary assessment for intelligent tutoring systems. Computers and Education, 53(3) pp. 890–899.

He, Yulan and Hui, Siu Cheung (2009). Exploring ant-based algorithms for gene expression data analysis. Artificial Intelligence in Medicine, 47(2) pp. 105–119.

He, Yulan and Chenghua, Lin (2009). Protein-protein interactions classification from text via local learning with class priors. In: 14th International Conference on Applications of Natural Language to Information Systems, 23-26 Jun 2009, Saarbrücken, Germany, pp. 182–191.

He, Yulan; Hui, Siu Cheung and Quan, Tho Thanh (2009). An ensemble approach for semantic assessment of summary writings. In: International Conference on Computer Engineering and Technology, 22-27 Jan 2009, Singapore.

He, Yulan; Nakata, Keiichi and Zhou, Deyu (2008). Ontology-based protein-protein interactions extraction from literature using the hidden vector state model. In: Workshop on Semantic Aspects in Data Mining at The IEEE International Conference on Data Mining series (ICDM 2008), 15-19 Dec 2008, Pisa, Italy.

Hui, Siu Cheung; He, Yulan and Dong, Haichao (2008). Text mining for chat message analysis. In: Cybernetics and Intelligent Systems, 2008 IEEE Conference on.

He, Yulan; Hui, Siu-Cheung and Toh, Hock Siang (2007). Ant-based association rule mining on gene expression data classification. In: Second IAPR International Workshop on Pattern Recognition in Bioinformatics (PRIB 2007), 1-2 Oct 2007, Singapore, China.

He, Yulan; Quan, Thanh Tho and Hui, Siu Cheung (2007). A multimodal restaurant finder for semantic web. In: 4th International Conference on Computing and Telecommunication Technologies (RIVF 2006), 12-16 Feb 2006, Ho Chi Minh city, Vietnam.

He, Yulan and Young, Steve (2006). Spoken language understanding using the Hidden Vector State Model. Speech Communication, 48(3-4) pp. 262–275.

He, Yulan; Hui, Siu Cheung and Sim, Yongxiang (2006). A novel ant-based clustering approach for document clustering. In: Information Retrieval Technology, Lecture Notes in Computer Science, Springer, pp. 537–544.

He, Yulan and Young, Steve (2005). Semantic processing using the Hidden Vector State Model. Computer Speech and Language, 19(1) pp. 85–106.

He, Yulan; Hui, Siu Cheung and Lai, Edmund Ming-Kit (2005). Automatic timetabling using artificial immune systems. In: Algorithmic Applications in Management, pp. 55–65.

He, Yulan and Young, Steve (2004). Robustness issues in a data-driven spoken language understanding system. In: HLT/NAACL 2004 Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing, 2-7 May 2004, Boston, MA, USA.

He, Yulan and Young, S. (2003). A data-driven spoken language understanding system. In: 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), pp. 583–588.

He, Yulan and Young, S. (2003). Hidden vector state model for hierarchical semantic parsing. Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference, 1 pp. 268–271.

He, Y.; Hui, S. C. and Fong, A. C. M. (2003). Citation-based retrieval for scholarly publications. IEEE Intelligent Systems, 18(2) pp. 58–65.

He, Yulan and Hui, Siu Cheung (2002). Mining a web citation database for author co-citation analysis. Information Processing & Management, 38(4) pp. 491–508.

He, Y.; Hui, S. C. and Fong, A. C. M. (2002). Mining a web citation database for document clustering. Applied Artificial Intelligence, 16(4) pp. 283–302.

He, Yulan and Hui, Siu Cheung (2001). PubSearch: a web citation-based retrieval system. Library Hi Tech, 19(3) pp. 274–285.

He, Yulan (2000). Mining citation database for the retrieval of scientific publications over the WWW. In: International Conference on Intelligent Information Processing, 21-25 Aug 2000, Beijing, China.

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Lin, Chenghua; He, Yulan; Everson, Richard and Rüger, Stefan (2012). Weakly-supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6) pp. 1134–1145.

Lin, Chenghua; He, Yulan; Pedrinaci, Carlos and Domingue, John (2012). Feature LDA: a supervised topic model for automatic detection of Web API documentations from the Web. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 Nov 2012, Boston, MA, USA, pp. 328–343.

Lin, Chenghua; He, Yulan and Everson, Richard (2011). Sentence subjectivity detection with weakly-supervised learning. In: The 5th International Joint Conference on Natural Language Processing (IJCNLP 2011), 08-13 Nov 2011, Chiang Mai, Thailand, pp. 1153–1161.

Lin, Chenghua; He, Yulan and Everson, Richard (2010). A comparative study of Bayesian models for unsupervised sentiment detection. In: The 14th Conference on Computational Natural Language Learning (CoNLL-2010), 15-16 Jul 2010, Uppsala, Sweden.

Lin, Chenghua and He, Yulan (2009). Joint sentiment/topic model for sentiment analysis. In: Proceeding of the 18th ACM conference on Information and knowledge management pp. 375–384.

Lukman, Suryani; He, Yulan and Hui, Siu-Cheung (2007). Computational methods for traditional Chinese medicine: a survey. Computer Methods and Programs in Biomedicine, 88(3) pp. 283–294.

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Moran, Stuart; He, Yulan and Liu, Kecheng (2009). Choosing the best Bayesian classifier: an empirical study. IAENG International Journal of Computer Science, 36(4) pp. 322–331.

Moran, Stuart; He, Yulan and Liu, Kecheng (2009). An empirical framework for selecting the best Bayesian classifier. In: International Conference on Data Mining and Knowledge Engineering, 01-03 Jul 2009, London.

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Saif, Hassan; He, Yulan; Fernández, Miriam and Alani, Harith (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52(1) pp. 5–19.

Saif, Hassan; He, Yulan; Fernández, Miriam and Alani, Harith (2014). Semantic patterns for sentiment analysis of Twitter. In: The Semantic Web – ISWC 2014, Springer International Publishing, pp. 324–340.

Saif, Hassan; He, Yulan; Fernández, Miriam and Alani, Harith (2014). Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter. In: The Semantic Web: ESWC 2014 Satellite Events, Springer International Publishing, pp. 54–63.

Saif, Hassan; Fernández, Miriam; He, Yulan and Alani, Harith (2014). SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter. In: The Semantic Web: Trends and Challenges: 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings, Lecture Notes in Computer Science, Springer Internatioal Publishing, pp. 83–98.

Saif, Hassan; Fernández, Miriam; He, Yulan and Alani, Harith (2014). On stopwords, filtering and data sparsity for sentiment analysis of Twitter. In: LREC 2014, Ninth International Conference on Language Resources and Evaluation. Proceedings., pp. 810–817.

Saif, Hassan; Fernández, Miriam; He, Yulan and Alani, Harith (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: 1st Interantional Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), 3 Dec 2013, Turin, Italy.

Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 Nov 2012, Boston, MA, USA.

Saif, Hassan; He, Yulan and Alani, Harith (2012). Alleviating data sparsity for Twitter sentiment analysis. In: 2nd Workshop on Making Sense of Microposts (#MSM2012): Big things come in small packages at the 21st International Conference on theWorld Wide Web (WWW'12), 16 Apr 2012, Lyon, France, CEUR Workshop Proceedings (CEUR-WS.org), pp. 2–9.

Saif, Hassan; He, Yulan and Alani, Harith (2011). Semantic smoothing for Twitter sentiment analysis. In: 10th International Semantic Web Conference (ISWC 2011), 23-27 Oct 2011, Bonn, Germany.

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Uren, Victoria; Wright, Daniel; Scott, James; He, Yulan and Saif, Hassan (2016). Social media and sentiment in bioenergy consultation. International Journal of Energy Sector Management, 10(1) pp. 87–98.

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Varga, Andrea; Cano Basave, Amparo Elizabeth; Rowe, Matthew; Ciravegna, Fabio and He, Yulan (2014). Linked knowledge sources for topic classification of microposts: a semantic graph-based approach. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 26 pp. 36–57.

Vuong, Ba-Quy; He, Yulan and Hui, Siu Cheung (2010). Towards a web-based progressive handwriting recognition environment for mathematical problem solving. Expert Systems with Applications, 37(1) pp. 886–893.

Vuong, Ba-Quy; Hui, Sui-Cheung and He, Yulan (2008). Progressive structural analysis for dynamic recognition of on-line handwritten mathematical expressions. Pattern Recognition Letters, 29(5) pp. 647–655.

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Zhou, Deyu and He, Yulan (2011). Biomedical events extraction using the hidden vector state model. Artificial Intelligence in Medicine, 53(3) pp. 205–213.

Zhou, Deyu and He, Yulan (2011). Learning conditional random fields from unaligned data for natural language understanding. In: 33RD European Conference on Information Retrieval (ECIR 2011), 18-21 Apr 2011, Dublin, Ireland.

Zhou, Deyu and He, Yulan (2011). Semantic parsing for biomedical event extraction. In: 9th International Conference on Computational Semantics, 12-14 Jan 2011, Oxford, UK.

Zhou, Deyu and He, Yulan (2011). A novel framework of training hidden Markov support vector machines from lightly-annotated data. In: 20th ACM Conference on Information and Knowledge Management (CIKM 2011), 24-28 Oct 2011, Glasgow, UK.

Zhou, Deyu and He, Yulan (2010). Analyzing ChIP-seq Data based on Multiple Knowledge Sources for Histone Modification. In: The 8th International Bioinformatics Workshop, 4-6 Jun 2010, Wuhan, China.

Zhou, Deyu and He, Yulan (2009). Discriminative training of the hidden vector state model for semantic parsing. IEEE Transactions on Knowledge and Data Engineering, 21(1) pp. 66–77.

Zhou, Deyu and He, Yulan (2008). Extracting protein-protein interaction based on discriminative training of the Hidden Vctor State model. In: ACL BioNLP Workshop, 19-20 Jun 2008, Columbus, OH, USA.

Zhou, Deyu and He, Yulan (2008). Extracting interactions between proteins from the literature. Journal of Biomedical Informatics, 41(2) pp. 393–407.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2008). Extracting protein-protein interactions from MEDLINE using the Hidden Vector State model. International Journal of Bioinformatics Research and Applications, 4(1) pp. 64–80.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2008). From biomedical literature to knowledge: mining protein-protein interactions. In: Smolinski, Tomasz G; Milanova, Mariofanna G and Hassanien, Aboul-Ella eds. Computational intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Studies in Computational Intelligence (151). Berlin: Springer, pp. 397–421.

Zhou, Deyu and He, Yulan (2008). A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus. In: The 22nd International Conference on Computational Linguistics (COLING 2008), 18-22 Aug 2008, Manchester.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Training the hidden vector state model from un-annotated corpus. In: The International Conference on Computational Science (ICCS 2007), 27-30 May 2007, Beijing, China.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Semi-supervised learning of the hidden vector state model for protein-protein interactions extraction. In: 2007 IEEE Symposium on Computational Intelligence and Data Mining, pp. 674–680.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Semi-supervised learning of the hidden vector state model for extracting protein-protein interactions. Artificial Intelligence in Medicine, 41(3) pp. 209–222.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Effective reranking for extracting protein-protein interactions from biomedical literature. In: Sixth International Conference on Bioinformatics (InCoB 2007), 27-31 Aug 2007, Hong Kong.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2006). Validating text mining results on protein-protein interactions using gene expression profiles. In: Proceedings of the 2006 International Conference on Biomedical & Pharmaceutical Engineering, Research Publishing Services, Singapore, pp. 580–585.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2006). An improved hidden vector state model approach and its adaptation in extracting protein interaction information from biomedical literature. In: ICCSB2006 : First International Conference on Computational Systems Biology, 20-23 Jul 2006, Shanghai, China.

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2006). Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model. In: Computational Science � ICCS 2006, pp. 718–725.

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