Edge-Based Best-First Chart Parsing

Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged ~'best" by some probabilistic figure of merit (FOM). Recent work has used probabilistic context-free grammars (PCFGs) to assign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to using a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results m that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing. 1 I n t r o d u c t i o n Finding one (or all) parses for a sentence according to a context-free grammar requires search. Fortunately, there are well known O(n 3) algorithms for parsing, where n is the length of the sentence. Unfortunately, for large grammars (such as the PCFG induced from the Penn II WSJ corpus, which contains around 1.6. i04 rules) and Iongish sentences (say, 40 words and punctuation), even O(n 3) looks pretty bleak. One well-known O(n 3) parsing method (Kay, 1980) is chart parsing. In this approach one maintains an agenda of items remaining to be " This material is based on work supported in past by NSF grants IRI-9319516 and SBR-9720368. and by ONR grant N0014-96.1-0549. processed, one of which is processed during each iteration. As each item is pulled off the agenda, it is added to the chart (unless it is already there, in which case it can be discarded) and used to extend and create additional items. In "exhaustive" chart parsing one removes items from the agenda in some relatively simple way (last-in, first-out is common), and continues to do so until nothing remains. A commonly discussed alternative is to remove the constituents from the agenda according to a figure of merit (FOM). The idea is that the FOM selects "good" items to be processed, leaving the ~'bad" ones-the ones that are not, in fact, part of the correct parse---sitting on the agenda. When one has a completed parse, or perhaps several possible parses, one simply stops parsing, leaving items remaining on the agenda. The time that would have been spent processing these remaining items is time saved, and thus time earned. In our work we have found that exhaustively parsing maximum-40-word sentences from the Penn II treebank requires an average of about 1.2 million edges per sentence. Numbers like this suggest that any approach that offers the possibility of reducing the work load is well worth pursuing, a fact that has been noted by several researchers. Early on, Kay (1980) suggested the use of the chart agenda for this purpose. More recently, the statistical approach to language processing and the use of probabilistic context-free grammars (PCFGs) has suggested using the PCFG probabilities to create a FOM. Bobrow (1990) and Chitrao and Grishman (1990) introduced best-first PCFG parsing, the approach taken here. Subsequent work has suggested different FOMs built from PCFG probabilities (Miller and Fox. 1994: Kochman and Kupin. 1991: Magerman and