tailieunhanh - Báo cáo khoa học: "Inducing Domain-specific Semantic Class Taggers from (Almost) Nothing"

This research explores the idea of inducing domain-specific semantic class taggers using only a domain-specific text collection and seed words. The learning process begins by inducing a classifier that only has access to contextual features, forcing it to generalize beyond the seeds. The contextual classifier then labels new instances, to expand and diversify the training set. Next, a cross-category bootstrapping process simultaneously trains a suite of classifiers for multiple semantic classes. . | Inducing Domain-specific Semantic Class Taggers from Almost Nothing Ruihong Huang and Ellen Riloff School of Computing University of Utah Salt Lake City UT 84112 huangrh riloff @ Abstract This research explores the idea of inducing domain-specific semantic class taggers using only a domain-specific text collection and seed words. The learning process begins by inducing a classifier that only has access to contextual features forcing it to generalize beyond the seeds. The contextual classifier then labels new instances to expand and diversify the training set. Next a cross-category bootstrapping process simultaneously trains a suite of classifiers for multiple semantic classes. The positive instances for one class are used as negative instances for the others in an iterative bootstrapping cycle. We also explore a one-semantic-class-per-discourse heuristic and use the classifiers to dynamically create semantic features. We evaluate our approach by inducing six semantic taggers from a collection of veterinary medicine message board posts. 1 Introduction The goal of our research is to create semantic class taggers that can assign a semantic class label to every noun phrase in a sentence. For example consider the sentence The lab mix was diagnosed with parvo and given abx . A semantic tagger should identify the the lab mix as an ANIMAL parvo as a DISEASE and abx antibiotics as a DRUG. Accurate semantic tagging could be beneficial for many NLP tasks including coreference resolution and word sense disambiguation and many NLP applications such as event extraction systems and question answering technology. Semantic class tagging has been the subject of previous research primarily under the guises of named entity recognition NER and mention detection. Named entity recognizers perform semantic tagging on proper name noun phrases and sometimes temporal and numeric expressions as well. The mention detection task was introduced in recent ACE evaluations . ACE 2007