Learning Computational Grammars

Learning Computational Grammars John Nerbonne � , Anja Belz � , Nicola Cancedda � , Herv´e D´ejean ,� James Hammerton � , Rob Koeling � , Stasinos Konstantopoulos � , Miles Osborne � , Franck Thollard � and Erik Tjong Kim Sang � Abstract This paper reports on the LEARNING COMPUTATIONAL GRAMMARS (LCG) project, a postdoc network devoted to studying the application of machine learning techniques to grammars suit- able for computational use. We were in- terested in a more systematic survey to understand the relevance of many fac- tors to the success of learning, esp. the availability of annotated data, the kind of dependencies in the data, and the availability of knowledge bases (gram- mars). We focused on syntax, esp. noun phrase (NP) syntax. 1 Introduction This paper reports on the still preliminary, but al- ready satisfying results of the LEARNING COM- PUTATIONAL GRAMMARS (LCG) project, a post- doc network devoted to studying the application of machine learning techniques to grammars suit- able for computational use. The member insti- tutes are listed with the authors and also included ISSCO at the University of Geneva. We were im- pressed by early experiments applying learning to natural language, but dissatisfied with the con- centration on a few techniques from the very rich area of machine learning. We were interested in � University of Groningen, � nerbonne,konstant � @let. rug.nl, osborne@cogsci.ed.ac.uk � SRI Cambridge, anja.belz@cam.sri.com, Rob.Koe- ling@netdecisions.co.uk XRCE Grenoble, nicola.cancedda@xrce.xerox.com University of T¨ubingen, Herve.Dejean@xrce.xerox. com, thollard@sfs.nphil.uni-tuebingen.de University College Dublin, james.hammerton@ucd.ie University of Antwerp, erikt@uia.ua.ac.be a more systematic survey to understand the rele- vance of many factors to the success of learning, esp. the availability of annotated data, the kind of dependencies in the data, and the availability of knowledge bases (grammars). We focused on syntax, esp. noun phrase (NP) syntax from the beginning. The industrial partner, Xerox, focused on more immediate applications (Cancedda and Samuelsson, 2000). The network was focused not only by its sci- entific goal, the application and evaluation of machine-learning techniques as used to learn nat- ural language syntax, and by the subarea of syn- tax chosen, NP syntax, but also by the use of shared training and test material, in this case ma- terial drawn from the Penn Treebank. Finally, we were curious about the possibility of combining different techniques, including those from statisti- cal and symbolic machine learning. The network members played an important role in the organi- sation of three open workshops in which several external groups participated, sharing data and test materials. 2 Method This section starts with a description of the three tasks that we have worked on in the framework of this project. After this we will describe the ma- chine learning algorithms applied to this data and conclude with some notes about combining dif- ferent system results. 2.1 Task descriptions In the framework of this project, we have worked on the following three tasks: 1. base phrase (chunk) identification 2. base noun phrase recognition 3. finding arbitrary noun phrases Text chunks are …

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