Marcus M
Department of Computer and Information Science, University of Pennsylvania, Philadelphia 19104-6389, USA.
Proc Natl Acad Sci U S A. 1995 Oct 24;92(22):10052-9. doi: 10.1073/pnas.92.22.10052.
The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.
在过去几年中，自然语言处理（NLP）领域在研究方向和方法上都发生了巨大的转变。过去，计算语言学中的大多数工作往往侧重于纯粹的符号方法。最近，越来越多的工作正在转向混合方法，这种方法将基于新语料库的新实证方法（包括概率和信息论技术的使用）与传统符号方法相结合。近期可用的语言数据库为自然语言文本语料库添加了丰富的语言注释，使得这项工作成为可能。这些方法已经使各种NLP系统的性能得到了显著提升，未来几年可能还会有类似的改进。本文重点关注这些趋势，特别概述了近期取得进展的三个领域：词性标注、随机句法分析和词汇语义学。
