Error Mining for Wide-Coverage Grammar Engineering

Error Mining for Wide-Coverage Grammar Engineering Gertjan van Noord Alfa-informatica University of Groningen POBox 716 9700 AS Groningen The Netherlands vannoord@let.rug.nl Abstract Parsing systems which rely on hand-coded linguis- tic descriptions can only perform adequately in as far as these descriptions are correct and complete. The paper describes an error mining technique to discover problems in hand-coded linguistic descrip- tions for parsing such as grammars and lexicons. By analysing parse results for very large unannotated corpora, the technique discovers missing, incorrect or incomplete linguistic descriptions. The technique uses the frequency of n-grams of words for arbitrary values of n. It is shown how a new combination of suffix arrays and perfect hash finite automata allows an efficient implementation. 1 Introduction As we all know, hand-crafted linguistic descriptions such as wide-coverage grammars and large scale dictionaries contain mistakes, and are incomplete. In the context of parsing, people often construct sets of example sentences that the system should be able to parse correctly. If a sentence cannot be parsed, it is a clear sign that something is wrong. This technique only works in as far as the problems that might occur have been anticipated. More recently, tree-banks have become available, and we can apply the parser to the sentences of the tree-bank and com- pare the resulting parse trees with the gold standard. Such techniques are limited, however, because tree- banks are relatively small. This is a serious prob- lem, because the distribution of words is Zipfian (there are very many words that occur very infre- quently), and the same appears to hold for syntactic constructions. In this paper, an error mining technique is de- scribed which is very effective at automatically dis- covering systematic mistakes in a parser by using very large (but unannotated) corpora. The idea is very simple. We run the parser on a large set of sen- tences, and then analyze those sentences the parser cannot parse successfully. Depending on the na- ture of the parser, we define the notion ‘success- ful parse’ in different ways. In the experiments described here, we use the Alpino wide-coverage parser for Dutch (Bouma et al., 2001; van der Beek et al., 2002b). This parser is based on a large con- structionalist HPSG for Dutch as well as a very large electronic dictionary (partly derived from CELEX, Parole, and CGN). The parser is robust in the sense that it essentially always produces a parse. If a full parse is not possible for a given sentence, then the parser returns a (minimal) number of parsed non- overlapping sentence parts. In the context of the present paper, a parse is called successful only if the parser finds an analysis spanning the full sentence. The basic idea is to compare the frequency of words and word sequences in sentences that can- not be parsed successfully with the frequency of the same words and word sequences in unproblematic sentences. As we illustrate in section 3, this tech- nique obtains very good results if it is applied …

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