Discourse Structures and Language Technologies

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 12-16. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

A Taxonomy of Academic Abstract Sentence Classification Modelling

Background: Abstract sentence classification modelling has the potential to advance literature discovery capability for the array of academic literature information systems, however, no artefact exists that categorises known models and identifies their key characteristics. Aims: To systematically categorise known abstract sentence classification models and make this knowledge readily available to future researchers and professionals concerned with abstract sentence classification model development and deployment. Method: An information systems taxonomy development methodology was adopted after a literature review to categorise 23 abstract sentence classification models identified from the literature. Corresponding dimensions and characteristics were derived from this process with the resulting taxonomy presented. Results: Abstract sentence classification modelling has evolved significantly with state-of-the-art models now leveraging neural networks to achieve high-performance sentence classification. The resulting taxonomy provides a novel means to observe the development of this research field and enables us to consider how such models can be further improved or deployed in real-world applications

Semantic metadata annotation. Tagging Medline abstracts for enhanced information access.

International audiencePurpose - The object of this study is to develop methods for automatically annotating the argumentative role of sentences in scientific abstracts. Working from Medline abstracts, sentences were classified into four major argumentative roles: objective, method, result, and conclusion. The idea is that, if the role of each sentence can be marked up, then these metadata can be used during information retrieval to seek particular types of information such as novelty, conclusions, methodologies, aims/goals of a scientific piece of work. Design/methodology/approach - Two approaches were tested: linguistic cues and positional heuristics. Linguistic cues are lexico-syntactic patterns modelled as regular expressions implemented in a linguistic parser. Positional heuristics make use of the relative position of a sentence in the abstract to deduce its argumentative class. Findings - The experiments showed that positional heuristics attained a much higher degree of accuracy on Medline abstracts with an F-score of 64 per cent, whereas the linguistic cues only attained an F-score of 12 per cent. This is mostly because sentences from different argumentative roles are not always announced by surface linguistic cues. Research limitations/implications - A limitation to the study was the inability to test other methods to perform this task such as machine learning techniques which have been reported to perform better on Medline abstracts. Also, to compare the results of the study with earlier studies using Medline abstracts, the different argumentative roles present in Medline had to be mapped on to four major argumentative roles. This may have favourably biased the performance of the sentence classification by positional heuristics. Originality/value - To the best of one's knowledge, this study presents the first instance of evaluating linguistic cues and positional heuristics on the same corpus

XIP Dashboard: visual analytics from automated rhetorical parsing of scientific metadiscourse

A key competency that we seek to build in learners is a critical mind, i.e. ability to engage with the ideas in the literature, and to identify when significant claims are being made in articles. The ability to decode such moves in texts is essential, as is the ability to make such moves in one’s own writing. Computational techniques for extracting them are becoming available, using Natural Language Processing (NLP) tuned to recognize the rhetorical signals that authors use when making a significant scholarly move. After reviewing related NLP work, we introduce the Xerox Incremental Parser (XIP), note previous work to render its output, and then motivate the design of the XIP Dashboard, a set of visual analytics modules built on XIP output, using the LAK/EDM open dataset as a test corpus. We report preliminary user reactions to a paper prototype of such a novel dashboard, describe the visualizations implemented to date, and present user scenarios for learners, educators and researchers. We conclude with a summary of ongoing design refinements, potential platform integrations, and questions that need to be investigated through end-user evaluations

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction

<p>Abstract</p> <p>Background</p> <p>This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases.</p> <p>Results</p> <p>Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%).</p> <p>Conclusions</p> <p>Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.</p

HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

<p>Abstract</p> <p>Background</p> <p>The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.</p> <p>Results</p> <p>We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.</p> <p>Conclusion</p> <p>Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.</p