Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.Comment: 2 pages, workshop paper accepted at the SIGIR 201

Overview of BioCreative II gene mention recognition.

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions

UCB: System Description for SemEval Task #4

The UC Berkeley team participated in the SemEval 2007 Task #4, with an approach that leverages the vast size of the Web in order to build lexically-specific features. The idea is to determine which verbs, prepositions, and conjunctions are used in sentences containing a target word pair, and to compare those to features extracted for other word pairs in order to determine which are most similar. By combining these Web features with words from the sentence context, our team was able to achieve the best result

Category-based pseudowords

A pseudoword is a composite comprised of two or more words chosen at random; the individual occurrences of the original words within a text are replaced by their conflation. Pseudowords are a useful mechanism for evaluating the im-pact of word sense ambiguity in many NLP applications. However, the standard method for constructing pseudowords has some draw-backs. Because the constituent words are cho-sen at random, the word contexts that surround pseudowords do not necessarily reflect the con-texts that real ambiguous words occur in. This in turn leads to an optimistic upper bound on algorithm performance. To address these draw-backs, we propose the use of lexical categories to create more realistic pseudowords, and eval-uate the results of different variations of this idea against the standard approach.

Category-Based Pseudowords

A pseudoword is a composite comprised of two or more words chosen at random; the individual occurrences of the original words within a text are replaced by their conflation. Pseudowords are a useful mechanism for evaluating the impact of word sense ambiguity in many NLP applications. However, the standard method for constructing pseudowords has some drawbacks. Because the constituent words are chosen at random, the word contexts that surround pseudowords do not necessarily reflect the contexts that real ambiguous words occur in. This in turn leads to an optimistic upper bound on algorithm performance. To address these drawbacks, we propose the use of lexical categories to create more realistic pseudowords, and evaluate the results of different variations of this idea against the standard approach

Citances: Citation Sentences for Semantic Analysis of Bioscience Text

We propose the use of the text of the sentences surrounding citations as an important tool for semantic interpretation of bioscience text. We hypothesize several di#erent uses of citation sentences (which we call citances), including the creation of training and testing data for semantic analysis (especially for entity and relation recognition), synonym set creation, database curation, document summarization, and information retrieval generally. We illustrate some of these ideas, showing that citations to one document in particular align well with what a hand-built curator extracted. We also show preliminary results on the problem of normalizing the di#erent ways that the same concepts are expressed within a set of citances, using and improving on existing techniques in automatic paraphrase generation

Biotext team report for the trec 2006 genomics track

The paper reports on the work conducted by the BioText team at UC Berkeley for the TREC 2006 Genomics track. Our approach had three main focal points