Description of the LTG system used for MUC-7

The basic building blocks in our muc system are reusable text handling tools which wehave been developing and using for a number of years at the Language Technology Group. They are modular tools with stream input/output; each tooldoesavery speci c job, but can be combined with other tools in a unix pipeline. Di erent combinations of the same tools can thus be used in a pipeline for completing di erent tasks. Our architecture imposes an additional constraint on the input/output streams: they should have a common syntactic format. For this common format we chose eXtensible Markup Language (xml). xml is an o cial, simpli ed version of Standard Generalised Markup Language (sgml), simpli ed to make processing easier [3]. Wewere involved in the developmentofthexml standard, building on our expertise in the design of our own Normalised sgml (nsl) and nsl tool lt nsl [10], and our xml tool lt xml [11]. A detailed comparison of this sgml-oriented architecture with more traditional data-base oriented architectures can be found in [9]. A tool in our architecture is thus a piece of software which uses an api for all its access to xml and sgml data and performs a particular task: exploiting markup which has previously been added by other tools, removing markup, or adding new markup to the stream(s) without destroying the previously adde

Bayesian Information Extraction Network

Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential of DBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain.Comment: 6 page

Generating Annotated Corpora for Reading Comprehension and Question Answering Evaluation

Recently, reading comprehension tests for students and adult language learners have received increased attention within the NLP community as a means to develop and evaluate robust question answering (NLQA) methods. We present our ongoing work on automatically creating richly annotated corpus resources for NLQA and on comparing automatic methods for answering questions against this data set. Starting with the CBC4Kids corpus, we have added XML annotation layers for tokenization, lemmatization, stemming, semantic classes, POS tags and bestranking syntactic parses to support future experiments with semantic answer retrieval and inference. Using this resource, we have calculated a baseline for word-overlap based answer retrieval (Hirschman et al., 1999) on the CBC4Kids data and found the method performs slightly better than on the REMEDIA corpus. We hope that our richly annotated version of the CBC4Kids corpus will become a standard resource, especially as a controlled environment for evaluating inference-based techniques

LT TTT - A Flexible Tokenisation Tool

We describe LT TTT, a recently developed software system which provides tools to perform text tokenisation and mark-up. The system includes ready-made components to segment text into paragraphs, sentences, words and other kinds of token but, crucially, it also allows users to tailor rule-sets to produce mark-up appropriate for particular applications. We present three case studies of our use of LT TTT: named-entity recognition (MUC-7), citation recognition and mark-up and the preparation of a corpus in the medical domain. We conclude with a discussion of the use of browsers to visualise marked-up text. 1. Introduction The LTG's Text Tokenisation Toolkit (LT TTT, Grover et al., 1999) was developed within an XML processing paradigm whereby tools are combined together in a pipeline allowing each to add, modify or remove some piece of mark-up. The tools are compatible with the LT XML toolset (Thompson et al., 1997) and use the LT XML API to manipulate attribute values and character data ..

Textual Information Extraction

Material completo EI

One, no one and one hundred thousand events: Defining and processing events in an inter-disciplinary perspective

We present an overview of event definition and processing spanning 25 years of research in NLP. We first provide linguistic background to the notion of event, and then present past attempts to formalize this concept in annotation standards to foster the development of benchmarks for event extraction systems. This ranges from MUC-3 in 1991 to the Time and Space Track challenge at SemEval 2015. Besides, we shed light on other disciplines in which the notion of event plays a crucial role, with a focus on the historical domain. Our goal is to provide a comprehensive study on event definitions and investigate which potential past efforts in the NLP community may have in a different research domain. We present the results of a questionnaire, where the notion of event for historians is put in relation to the NLP perspective