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.

Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.Comment: 12 pages, 2 figure

Open Domain Event Extraction Using Neural Latent Variable Models

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.Comment: accepted by ACL 201

Creating corroborated crisis reports from social media data through formal concept analysis

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake. Keywords : formal concept analysis, crisis management, disaster response, visualisation, entity extraction

Event representation across genre

Peer reviewe

Learning to Automatically Solve Algebra Word Problems

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task.Battelle Memorial Institute (PO 300662)National Science Foundation (U.S.) (Grant IIS-0835652