Memory-Based Shallow Parsing

Memory-Based Shallow Parsing Walter Daelemans, Sabine Buchholz, Jorn Veenstra ILK, Tilburg University, PO-box 90153, NL 5000 LE Tilburg [walt er, buchho lz, ve enstra@kub, nl] Abstract We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F~=l for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection. Introduction Recently, there has been an increased interest in ap- proaches to automatically learning to recognize shallow linguistic patterns in text [Ramshaw and Marcus, 1995, Vilain and Day, 1996, Argamon et al., 1998, Buchholz, 1998, Cardie and Pierce, 1998, Veenstra, 1998, Daelemans et aI., 1999a]. Shallow parsing is an important component of most text analysis systems in applications such as information extraction and summary generation. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like subject, object, adjunct relations between verbs and heads of other constituents. Memory-Based Learning (MBL) shares with other statistical and learning techniques the advantages of avoiding the need for manual definition of patterns (common practice is to use hand-crafted regular expres- sions), and of being reusable for different corpora and sublanguages. The unique property of memory-based approaches which sets them apart from other learn- ing methods is the fact that they are lazy learners: they keep all training data available for extrapolation. All other statistical and machine learning methods are eager (or greedy) learners: They abstract knowledge structures or probability distributions from the train- ing data, forget the individual training instances, and extrapolate from the induced structures. Lazy learn- ing techniques have been shown to achieve higher ac- curacy than eager methods for many language pro- cessing tasks. A reason for this is tile intricate in- teraction between regularities, subregularities and ex- ceptions in most language data. and the related prob- lem for learners of distinguishing noise from excep- tions. Eager learning techniques abstract from what they consider noise (hapaxes, low-frequency events, non-typical events) whereas lazy learning techniques keep all data available, including exceptions which may sometimes be productive. For a detailed analy- sis of this issue, see [Daelemans et al., 1999a]. More- over, the automatic feature weighting in the similar- ity metric of a memory-based learner makes the ap- proach well-suited for domains with large numbers of features from heterogeneous sources, as it embodies a smoothing-by-similarity method when data is sparse [Zavrel and Daelemans, 1997]. In this paper, we will provide a empirical evalua- tion of tile MBL approach to syntactic analysis on a number of shallow pattern learning tasks: NP chunk- ing, \'P clmnking, and the assignment of subject-verb and object-verb relations. The approach is evalu- ated by cross-validation on the WSJ treebank corpus [Marcus et al., 1993]. We compare the approach quali- tatively and as far as possible quantitatively with other approaches. Memory-Based Shallow Syntactic Analysis Memory-Based …

Content truncated to 500 words