Information extraction from research papers using conditional random fields

Peng, Fuchun; McCallum, Andrew

doi:10.1016/j.ipm.2005.09.002

Cited by 266 publications

(233 citation statements)

References 14 publications

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“…Plagiarism detection, like many research-paper recommenders, uses text and citation analysis to identify similar documents [252][253][254]. Additionally, research relating to crawling the web and analyzing academic ar6cles can be useful for building research-paper recommender systems, for instance, author 1 4 [255], title extraction [256][257][258][259][260], or citation extraction and matching [261]. Finally, most of the research on content-based [262] or collaborative filtering [263,264] from other domains, such as movies or news, can also be relevant for research-paper recommender systems.…”

Section: R Elated Research Fieldsmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: R Elated Research Fieldsmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…A natural path to be improved in the system is the use of information from other more general or semi-structured sources. In that sense, we can explore new methods in regard to automatically extract information 7,27 .…”

Section: Discussionmentioning

confidence: 99%

“…Many works have been devoted to the analysis of co-authorship and collaborations from papers databases. Many of the issues in this research appear from the low degree of structure of the data as well as the ambiguities often present 9,22,27 . Another important issue is the analysis of the networks themselves 14,17,20,28 .…”

Section: Introductionmentioning

confidence: 99%

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

Mena‐Chalco¹,

César²

2009

J. Braz. Comp. Soc.

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Abstract:The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.

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“…Just like SVMs, CRFs can "handle many dependent features"; however, unlike SVMs, they also can "make joint inference over entire sequences" [24]. In our case, predictions over entire sequences correspond to global context.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A de-identifier for medical discharge summaries

Uzuner

Sibanda

Luo

et al. 2008

Artificial Intelligence in Medicine

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Objective-Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals.The goal of de-identification is to remove all PHI from clinical records. This is a challenging task because many records contain foreign and misspelled PHI; they also contain PHI that are ambiguous with non-PHI. These complications are compounded by the linguistic characteristics of clinical records. For example, medical discharge summaries, which are studied in this paper, are characterized by fragmented, incomplete utterances and domain-specific language; they cannot be fully processed by tools designed for lay language.Methods and Results-In this paper, we show that we can de-identify medical discharge summaries using a de-identifier, Stat De-id, based on support vector machines and local context (Fmeasure = 97% on PHI). Our representation of local context aids de-identification even when PHI include out-of-vocabulary words and even when PHI are ambiguous with non-PHI within the same corpus. Comparison of Stat De-id with a rule-based approach shows that local context contributes more to de-identification than dictionaries combined with hand-tailored heuristics (F-measure = 85%). Comparison with two well-known named entity recognition (NER) systems, SNoW (Fmeasure = 94%) and IdentiFinder (F-measure = 36%), on five representative corpora show that when the language of documents is fragmented, a system with a relatively thorough representation of local context can be a more effective de-identifier than systems that combine (relatively simpler) local context with global context.

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Information extraction from research papers using conditional random fields

Cited by 266 publications

References 14 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

A de-identifier for medical discharge summaries

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