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- tutorialAugust 2015
Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 2319–2320https://doi.org/10.1145/2783258.2789988In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of ...
- research-articleAugust 2015
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
- Meenakshi Nagarajan,
- Angela D. Wilkins,
- Benjamin J. Bachman,
- Ilya B. Novikov,
- Shenghua Bao,
- Peter J. Haas,
- María E. Terrón-Díaz,
- Sumit Bhatia,
- Anbu K. Adikesavan,
- Jacques J. Labrie,
- Sam Regenbogen,
- Christie M. Buchovecky,
- Curtis R. Pickering,
- Linda Kato,
- Andreas M. Lisewski,
- Ana Lelescu,
- Houyin Zhang,
- Stephen Boyer,
- Griff Weber,
- Ying Chen,
- Lawrence Donehower,
- Scott Spangler,
- Olivier Lichtarge
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 2019–2028https://doi.org/10.1145/2783258.2788609We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the ...
- research-articleAugust 2015
Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1779–1788https://doi.org/10.1145/2783258.2788608We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-...
- research-articleAugust 2015
Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1711–1720https://doi.org/10.1145/2783258.2788607We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agent's speed from ...
- research-articleAugust 2015
Spoken English Grading: Machine Learning with Crowd Intelligence
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 2089–2097https://doi.org/10.1145/2783258.2788595In this paper, we address the problem of grading spontaneous speech using a combination of machine learning and crowdsourcing. Traditional machine learning techniques solve the stated problem inadequately as automatic speaker-independent speech ...
- research-articleAugust 2015
Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 2257–2266https://doi.org/10.1145/2783258.2788580Business-to-consumer (B2C) emails are usually generated by filling structured user data (e.g.purchase, event) into templates. Extracting structured data from B2C emails allows users to track important information on various devices.
However, it also ...
- research-articleAugust 2015
Inside Jokes: Identifying Humorous Cartoon Captions
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1065–1074https://doi.org/10.1145/2783258.2783388Humor is an integral aspect of the human experience. Motivated by the prospect of creating computational models of humor, we study the influence of the language of cartoon captions on the perceived humorousness of the cartoons. Our studies are based on ...
- research-articleAugust 2015
From Group to Individual Labels Using Deep Features
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 597–606https://doi.org/10.1145/2783258.2783380In many classification problems labels are relatively scarce. One context in which this occurs is where we have labels for groups of instances but not for the instances themselves, as in multi-instance learning. Past work on this problem has typically ...
- research-articleAugust 2015
Multi-Task Learning for Spatio-Temporal Event Forecasting
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1503–1512https://doi.org/10.1145/2783258.2783377Spatial event forecasting from social media is an important problem but encounters critical challenges, such as dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different ...
