New Resources and Perspectives for Biomedical Event Extraction

Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extraction of events from text. We introduce a new biomedical event extraction resource consisting of analyses automatically created by systems participating in the recent BioNLP Shared Task (ST) 2011. In providing for the first time the outputs of a broad set of state-ofthe-art event extraction systems, this resource opens many new opportunities for studying aspects of event extraction, from the identification of common errors to the study of effective approaches to combining the strengths of systems. We demonstrate these opportunities through a multi-system analysis on three BioNLP ST 2011 main tasks, focusing on events that none of the systems can successfully extract. We further argue for new perspectives to the performance evaluation of domain event extraction systems, considering a document-level, “off-the-page ” representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.

Audio-visual foreground extraction for event characterization

This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities

Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.Comment: The work has been accepted in BioNLP at ACL-201

Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201

Doctor of Philosophy

dissertationEvents are one important type of information throughout text. Event extraction is an information extraction (IE) task that involves identifying entities and objects (mainly noun phrases) that represent important roles in events of a particular type. However, the extraction performance of current event extraction systems is limited because they mainly consider local context (mostly isolated sentences) when making each extraction decision. My research aims to improve both coverage and accuracy of event extraction performance by explicitly identifying event contexts before extracting individual facts. First, I introduce new event extraction architectures that incorporate discourse information across a document to seek out and validate pieces of event descriptions within the document. TIER is a multilayered event extraction architecture that performs text analysis at multiple granularities to progressively \zoom in" on relevant event information. LINKER is a unied discourse-guided approach that includes a structured sentence classier to sequentially read a story and determine which sentences contain event information based on both the local and preceding contexts. Experimental results on two distinct event domains show that compared to previous event extraction systems, TIER can nd more event information while maintaining a good extraction accuracy, and LINKER can further improve extraction accuracy. Finding documents that describe a specic type of event is also highly challenging because of the wide variety and ambiguity of event expressions. In this dissertation, I present the multifaceted event recognition approach that uses event dening characteristics (facets), in addition to event expressions, to eectively resolve the complexity of event descriptions. I also present a novel bootstrapping algorithm to automatically learn event expressions as well as facets of events, which requires minimal human supervision. Experimental results show that the multifaceted event recognition approach can eectively identify documents that describe a particular type of event and make event extraction systems more precise

Computational Tools for Supersymmetry Calculations

I present a brief overview of a variety of computational tools for supersymmetry calculations, including: spectrum generators, cross section and branching fraction calculators, low energy constraints, general purpose event generators, matrix element event generators, SUSY dark matter codes, parameter extraction codes and Les Houches interface tools.Comment: Chapter to appear in Perspectives on Supersymmetry, edited by G. Kane; 23 pages including one .eps figur