Language Independent NER using a Unified Model of Internal and Contextual Evidence

Language Independent NER using a Unified Model of Internal and Contextual Evidence Silviu Cucerzan and David Yarowsky Department of Computer Science and Center for Language and Speech Processing Johns Hopkins University Baltimore, MD 21218, USA {silviu,yarowsky}@cs.jhu.edu Abstract This paper investigates the use of a language inde- pendent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as inde- pendent evidence sources. Its bootstrapping pro- cess begins with only seed entities and seed con- texts extracted from the provided annotated corpus. F-measure exceeds 77 in Spanish and 72 in Dutch. 1. Introduction Our aim has been to build a maximally language- independent system for named-entity recognition using minimal supervision or knowledge of the source language. The core model utilized, ex- tended and evaluated here is based on Cucerzan and Yarowsky (1999). It assumes that only an entity ex- emplar list is provided as a bootstrapping seed set. For the particular task of CoNLL-2002, the seed entities are extracted from the provided annotated corpus. As a consequence, the seed examples may be ambiguous and the system must therefore han- dle seeds with probability distribution over entity classes rather than unambiguous seeds. Another consequence is that this approach of extracting only the entity seeds from the annotated text does not use the full potential of the training data, ignoring con- textual information. For example, Bosnia appears labeled 9 times as LOC and 5 times as ORG and the only information that would be used is that the word Bosnia denotes a location 64% of the time, and an organization 36% of the time, but not in which contexts is labeled one way or the other. In order to correct this problem, an improved system also uses context seeds if available (for this particu- lar task, they are extracted from the annotated cor- pus). Because the representations of entity candi- dates and contexts are identical, this modification imposes only minor changes in algorithm and code. Because the core model has been presented in de- tail in Cucerzan and Yarowsky (1999), this paper focuses primarily on the modifications of the algo- rithm and its adaptation to the current task. The ma- jor modifications besides the seed handling include a different method of smoothing the distributions along the paths in the tries, a new ’soft’ discourse segmentation method, and use of a different label- ing methodology, as required by the current task i.e. no overlapping entities are allowed (for example, the correct labeling of colegio San Juan Bosco de Mérida is considered to be ORG(colegio San Juan Bosco) de LOC(Mérida) rather than ORG(colegio PER(San Juan Bosco) de LOC(Mérida))). 2. Entity-Internal Information Two types of entity-internal evidence are used in a unified framework. The first consists of the pre- fixes and suffixes of candidate entities. For exam- ple, in Spanish, names ending in -ez (e.g. Alvarez and Gutiérrez) are often surnames; names ending in -ia are often locations (e.g. Austria, Australia, and Italia). Likewise, common beginnings and endings …

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