MUC

This paper describes RoboTag, an ad- vanced prototype for a machine learning- based multilingual information extraction system. First, we describe a general client/server architecture used in learning from observation. Then …...

Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and …...

We present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing MUC (Message Understand- ing Conference) Information Extraction system, allowing quantitative evaluation against a …...

This paper investigates two strategies for im- proving coreference resolution: (1) training separate models that specialize in particu- lar types of mentions (e.g., pronouns versus proper nouns) and (2) using …...

Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated …...

Named entity recognition (NER) for En- glish typically involves one of three gold standards: MUC, CoNLL, or BBN, all created by costly manual annotation. Recent work has used Wikipedia to …...

AbstractThe term “event extraction” covers a wide range of information extraction tasks, and methods developed and evaluated for one task may prove quite unsuitable for another. Understanding these task differences …...

This article explores the portability of a coreference resolver across a variety of eight text genres. Besides newspaper text, we also include administrative texts, au- tocues, texts used for external …...

This paper presents a system based on Finite State Technology that recognises and classifies numerical entities in texts written in Basque. The system deals with a wide range of entities, …...

We develop a people-centered computational history of science that tracks authors over top- ics and apply it to the history of computa- tional linguistics. We present four findings in this …...

This paper proposes a new method for significantly improving the performance of pairwise coreference models. Given a set of indicators, our method learns how to best separate types of mention …...

Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are of- ten caused by superficial modeling …...

Interpretability and discriminative power are the two most basic requirements for an evaluation metric. In this paper, we re- port the mention identification effect in the B3, CEAF, and BLANC …...

AbstractWe propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical …...

AbstractExtracting relevant user behaviors through customer’s transaction description is one of the ways to collect customer information. In the current text mining field, most of the researches are mainly study …...