Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries
会议地点: Ann Arbor, MI, USA
The goal of the BIRNDL workshop at SIGIR is to engage the IR community about the open problems in academic search. Academic search refers to the large, cross-domain digital repositories which index research papers, such as the ACL Anthology, ArXiv, ACM Digital Library, IEEE database, Web of Science and Google Scholar. Currently, digital libraries collect and allow access to papers and their metadata --- including citations --- but mostly do not analyze the items they index. The scale of scholarly publications poses a challenge for scholars in their search for relevant literature. Finding relevant scholarly literature is the key theme of BIRNDL and sets the agenda for tools and approaches to be discussed and evaluated at the workshop. We would also like to address the need for established, standardized baselines, evaluation metrics and test collections.
We invite papers and presentations that incorporate insights from IR, bibliometrics and NLP to develop new techniques to address the open problems in Big Science, such as evidence-based searching, measurement of research quality, relevance and impact, the emergence and decline of research problems, identification of scholarly relationships and influences and applied problems such as language translation, question-answering and summarization.
We invite stimulating as well as unpublished submissions on topics including - but not limited to - full-text analysis, multimedia and multilingual analysis and alignment as well as the application of citation-based NLP or information retrieval and information seeking techniques in digital libraries. Specific examples of fields of interests include (but are not limited to):
-Infrastructure for scientific mining and IR
-Semantic and Network-based indexing, navigation, searching and browsing in structured data
-Discourse structure identification and argument mining from scientific papers
-Summarisation and question-answering for scholarly DLs;
-Bibliometrics, citation analysis and network analysis for IR
-Task based user modelling, interaction, and personalisation
-Recommendation for scholarly papers, reviewers, citations and publication venues
-Measurement and evaluation of quality and impact
-Metadata and controlled vocabularies for resource description and discovery; Automatic metadata discovery, such as language identification
-Disambiguation issues in scholarly DLs using NLP or IR techniques; Data cleaning and data quality
-Evaluation baselines, metrics and test collections for research problems in IR
For the paper sessions, we especially invite descriptions of running projects and ongoing work as well as contributions from industry. Papers that investigate multiple themes directly are especially welcome.