The biomedical discourse relation bank

<p>Abstract</p> <p>Background</p> <p>Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource.</p> <p>Results</p> <p>We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89). These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28), mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57).</p> <p>Conclusion</p> <p>Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more annotated data. The poor performance of a classifier trained in the open domain and tested in the biomedical domain suggests significant differences in the semantic usage of connectives across these domains, and provides robust evidence for a biomedical sublanguage for discourse and the need to develop a specialized biomedical discourse annotated corpus. The results of our cross-domain experiments are consistent with related work on identifying connectives in BioDRB.</p

On Recognizing Argumentation Schemes in Formal Text Genres

Argumentation mining research should address the challenge of recognition of argumentation schemes in formal text genres such as scientific articles. This paper argues that identification of argumentation schemes differs from identification of other aspects of discourse such as argumentative zones and coherence relations. Argumentation schemes can be defined at a level of abstraction applicable across the natural sciences. There are useful applications of automatic argumentation scheme recognition. However, it is likely that inference-based techniques will be required.(Note: Due to a publishing error, only the abstract of the paper appeared in the published Seminar Report at http://dx.doi.org/10.4230/DagRep.6.4.80

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Registerial cartography: context-based mapping of text types and their rhetorical-relational organization

Department of EnglishInvited conference pape

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

What excludes an Alternative in Coherence Relations?

This paper identifies features that occur frequently in coherence relations labelled CHOSEN AL-TERNATIVE. This achieves two goals: (1) to identify evidence for an argument being considered an alternative excluded from further consideration, and (2) to contribute to the automatic identification of coherence relations and their arguments. It is shown that the simplest of these features occur significantly more often in implicit CHOSEN ALTERNATIVE relations than in explicit CHOSEN ALTERNATIVE relations, where a connective helps signal this sense.

Towards event-based discourse analysis of biomedical text

Annotating biomedical text with discourse-level information is a well-studied topic. Several research efforts have annotated textual zones (e.g., sentences or clauses) with information about rhetorical status, whilst other efforts have linked and classified sets of text spans according to the type of discourse relation holding between them. A relatively new approach has involved annotating meta-knowledge (i.e., rhetorical intent and other types of information concerning interpretation) at the level of bio-events, which are structured representations of pieces of biomedical knowledge. In this paper, we report on the examination and comparison of transitions and patterns of event metaknowledge values that occur in both abstracts and full papers. Our analysis highlights a number of specific characteristics of event-level discourse patterns, as well as several noticeable differences between the types of patterns that occur in abstracts and full papers

Enriching news events with meta-knowledge information

Given the vast amounts of data available in digitised textual form, it is important to provide mechanisms that allow users to extract nuggets of relevant information from the ever growing volumes of potentially important documents. Text mining techniques can help, through their ability to automatically extract relevant event descriptions, which link entities with situations described in the text. However, correct and complete interpretation of these event descriptions is not possible without considering additional contextual information often present within the surrounding text. This information, which we refer to as meta-knowledge, can include (but is not restricted to) the modality, subjectivity, source, polarity and specificity of the event. We have developed a meta-knowledge annotation scheme specifically tailored for news events, which includes six aspects of event interpretation. We have applied this annotation scheme to the ACE 2005 corpus, which contains 599 documents from various written and spoken news sources. We have also identified and annotated the words and phrases evoking the different types of meta-knowledge. Evaluation of the annotated corpus shows high levels of inter-annotator agreement for five meta-knowledge attributes, and moderate level of agreement for the sixth attribute. Detailed analysis of the annotated corpus has revealed further insights into the expression mechanisms of different types of meta-knowledge, their relative frequencies and mutual correlations