Preliminary Conference Agenda

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Session Overview
Papers 20: Data Mining and NLP
Tuesday, 02/Apr/2019:
3:30pm - 5:00pm

Session Chair: Catherine Blake, Illinois
Location: 2100/2101/2102

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Identifying Finding Sentences in Conclusion Subsections of Biomedical Abstracts

Y. LI, B. YU

Syracuse University, United States of America

Segmenting scientific abstracts and full-text based on their rhetorical function is an essential task in text classification. Small rhetorical segments can be useful for fine-grained literature search, summarization, and comparison. Current effort has been focusing on segmenting documents into general sections such as introduction, method, and conclusion, and much less on the roles of individual sentences within the segments. For example, not all sentences in the conclusion section are describing research findings. In this work, we developed rule-based and machine learning methods and compared their performance in identifying the finding sentences in conclusion subsections of biomedical abstracts. 1100 conclusion subsections with observational and randomized clinical trials study designs covering five common health topics were sampled from PubMed to develop and evaluate the methods. The rule-based method and the bag-of-words based machine learning method both achieved high accuracy. The better performance by the simple rule-based approach shows that although advanced machine learning approaches could capture the main patterns, human expert may still outperform on such a specialized task.

How to Make a Successful Movie: Factor Analysis from Both Financial and Critical Perspectives

Z. Gao1, V. Malic1, S. Ma2, P. Shih1

1indiana university bloomington, United States of America; 2Nanjing University of Science and Technology

Over the past twenty years, people have seen considerable growth in film industry. There are two common measurements for movie quality, financial metric of net profit and reception metric in the form of ratings assigned by moviegoers on websites. Researchers have utilized these two metrics to build models for movie success prediction separately, while few of them investigate the combination. Therefore, in this paper, we analyze movie success from perspectives of financial and critical metrics in tandem. Here, optimal success is defined as a film that is both profitable and highly acclaimed, while its worst outcome involves financial loss and critical panning at the same time. Salient features that are salient to both financial and critical outcomes are identified in an attempt to uncover what makes a "good'' movie "good'' and a "bad'' one ``bad'' as well as explain common phenomenons in movie industry quantitatively.

Authority Claim in Rationale-Containing Online Comments

L. Xiao, X. Huo

Syracuse University, United States of America

We examined whether the existence of authority claims signifies one’s ra-tionales in online communication content, potentially contributing to the re-search on rationale identification and rationale generation. Authority claims are statements that reveal the writer’s intention to bolster the writer’s credibil-ity. In open online communications, the anonymity and the dynamic partici-pation make it challenging to establish the credibility of their viewpoints and reasoning. Therefore, we hypothesize these online participants will tend to use authority claims to bolster their credibility when presenting their justifica-tions. We annotated authority claims in 271 text segments that contain online users’ rationales. These text segments are adapted from the open access cor-pora provided by Rutgers’ Argument Mining group. Contrary to our hypothe-sis, we found that in our dataset the users scarcely attempted to bolster their credibility when presenting their reasoning to the others in these activities. We call for more investigations to explore the role of activity context affects participants’ use of authority claims in their reasoning traces. We further state that the effects of communication medium on individuals’ cognitive and meta-cognitive processes are important to consider in argument mining research.