期刊名称：International Journal of Intelligence Science
印刷版ISSN：2163-0283
电子版ISSN：2163-0356
出版年度：2012
卷号：2
期号：4A
页码：190-202
DOI：10.4236/ijis.2012.224025
出版社：Scientific Research Publishing
摘要：One of the most useful Information Extraction (IE) solutions to Web information harnessing is Named Entity Recognition (NER). Hand-coded rule methods are still the best performers. These methods and statistical methods exploit Natural Language Processing (NLP) features and characteristics (e.g. Capitalization) to extract Named Entities (NE) like personal and company names. For entities with multiple sub-entities of higher cardinality (e.g. linux command, citation) and which are non-speech, these systems fail to deliver efficiently. Promising Machine Learning (ML) methods would require large amounts of training examples which are impossible to manually produce. We call these entities Named High Cardinality Entities (NHCEs). We propose a sequence validation based approach for the extraction and validation of NHCEs. In the approach, sub-entities of NHCE candidates are statistically and structurally characterized during top-down annotation process and guided to transformation into either value types (v-type) or user-defined types (u-type) using a ML model. Treated as sequences of sub-entities, NHCE candidates with transformed sub-entities are then validated (and subsequently labeled) using a series of validation operators. We present a case study to demonstrate the approach and show how it helps to bridge the gap between IE and Intelligent Systems (IS) through the use of transformed sub-entities in supervised learning.
关键词：Entity Recognition; Supervised Learning; Sequence Validation; Intelligent Systems; Text Mining
