1997/06/11 by Adwait Ratnaparkhi, Ratnaparkhi, Adwait · 3 citations
Computer Science · Engineering · #Algorithms and Data Compression #Computation and Language (cs.CL) #FOS: Computer and information sciences #Fault Detection and Control Systems #Natural Language Processing Techniques #Topic Modeling #cmp-lg #cs.CL
paper · pdf · doi:10.48550/arxiv.cmp-lg/9706014
10 pages, LaTeX, uses aclap.sty, qtree.sty, to appear in EMNLP-2
arxiv created 1997/06/11 · openalex publication_date 1997/06/11 · arxiv updated 2016/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a statistical parser for natural language that obtains a parsing accuracy---roughly 87% precision and 86% recall---which surpasses the best previously published results on the Wall St. Journal domain. The parser itself requires very little human intervention, since the information it uses to make parsing decisions is specified in a concise and simple manner, and is combined in a fully automatic way under the maximum entropy framework. The observed running time of the parser on a test sentence is linear with respect to the sentence length. Furthermore, the parser returns several scored parses for a sentence, and this paper shows that a scheme to pick the best parse from the 20 highest scoring parses could yield a dramatically higher accuracy of 93% precision and recall.