Identity and Granularity of Events in Text

In this paper we describe a method to detect event descrip- tions in different news articles and to model the semantics of events and their components using RDF representations. We compare these descriptions to solve a cross-document event coreference task. Our com- ponent approach to event semantics defines identity and granularity of events at different levels. It performs close to state-of-the-art approaches on the cross-document event coreference task, while outperforming other works when assuming similar quality of event detection. We demonstrate how granularity and identity are interconnected and we discuss how se- mantic anomaly could be used to define differences between coreference, subevent and topical relations.Comment: Invited keynote speech by Piek Vossen at Cicling 201

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download

University of Sheffield TREC-8 Q & A System

The system entered by the University of Sheffield in the question answering track of TREC-8 is the result of coupling two existing technologies - information retrieval (IR) and information extraction (IE). In essence the approach is this: the IR system treats the question as a query and returns a set of top ranked documents or passages; the IE system uses NLP techniques to parse the question, analyse the top ranked documents or passages returned by the IR system, and instantiate a query variable in the semantic representation of the question against the semantic representation of the analysed documents or passages. Thus, while the IE system by no means attempts “full text understanding", this approach is a relatively deep approach which attempts to work with meaning representations. Since the information retrieval systems we used were not our own (AT&T and UMass) and were used more or less “off the shelf", this paper concentrates on describing the modifications made to our existing information extraction system to allow it to participate in the Q & A task

Distantly Labeling Data for Large Scale Cross-Document Coreference

Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on ``distantly-labeling'' a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant labeling with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has factors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.Comment: 16 pages, submitted to ECML 201

Coreference resolution in clinical discharge summaries, progress notes, surgical and pathology reports: a unified lexical approach

We developed a lexical rule-based system that uses a unified approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA) provided for the fifth i2b2/VA shared task. Taking the unweighted mean between 4 coreference metrics, validation of the system against the i2b2/VA corpus attained an overall F-score of 87.7% across all mention classes, with a maximum of 93.1% for coreference of persons, and a minimum of 77.2% for coreference of tests. For the ODIE corpus the overall F-score across all mention classes was 79.4%, with a maximum of 82.0% for coreference of persons and a minimum of 13.1% for coreference of diagnostic reagents. For the ODIE corpus our results are comparable to the mean reported inter-annotator agreement with the gold standard. We discuss the four categories of errors we identified, and how these might be addressed. The system uses a number of reusable modules and techniques that may be of benefit to the research community