The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature...
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Download Citation | A Pool of Topics: Interactive Relational Topic Visualization for Information Discovery | In this paper, we present a novel relational visualization that supports people at information discovery tasks in digital libraries. This... | Find, read and cite all the research you...
This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based...
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Kathleen McKeown, Sara Rosenthal, Kapil Thadani, Coleman Moore. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2010.
"Recent research has investigated methods for generating new sentences using a technique called sentence fusion (Barzilay and McKeown, 2005; Marsi and Krahmer, 2005; Filippova and Strube, 2008) where output sentences are generated by fusing together portions of related sentences."
"Our goal is the generation of accurate fusions between pairs of sentences that have some information in common. To ensure that the task is performed consistently, we abide by the distinction proposed by Marsi and Krahmer (2005) between intersection fusion and union fusion. Intersection fusion results in a sentence that contains only the information that the sentences had in common and is usually shorter than either of the original sentences. Union fusion, on the other hand, results in a sentence that contains all information content from the original two sentences."
"When sentences are too similar, the result of fusion is simply one of the input sentences. For example (Fig. 2), if sentence A contains all the information in sentence B but not vice versa, then B is also their intersection while A is their union and no sentence generation is required. On the other hand, if the two sentences are too dissimilar, then no intersection is possible and the union is just the conjunction of the sentences."
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes. Jie Cao, Michael Tanana, Zac Imel, Eric Poitras, David Atkins and Vivek Srikumar. Annual Meeting of the Association for Computational Linguistics (ACL), 2019. [pdf][details]
Modeling Biological Processes for Reading Comprehension. Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark and Christopher D. Manning. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. [pdf][details][Best paper award]