ROBUST PARSING | A BRIEF OVERVIEW

Parsing systems able to analyse natural language text robustly and accurately at an appropriate level of detail would be of great value in computer applications ranging from speech synthesis and document style checking to message understanding and automatic translation. A number of research groups worldwide are currently developing such systems, varying in the depth of analysis from lexical parsing or tagging (identifying syntactic features just of individual words), through shallow or phrasal parsing (finding phrases, e.g. NPs, or forming hierarchical syntactic structure but not exploiting subcategorisation), to full parsers (which deal with unbounded dependencies etc., and are able to recover predicate-argument structure). However, despite over three decades of research effort, no practical domain-independent parser of unrestricted text has been developed. Such a parser should return the correct or a useful ‘close’ analysis for 90% or more of input sentences. It would need to solve at least the following three problems, which create severe difficulties for conventional parsers utilising standard parsing algorithms with a generative grammar (for general background see Gazdar & Mellish, 1989): appropriate segmentation of text into syntactically parseable units; disambiguation, that is, selecting the unique semantically and pragmatically correct analysis from the potentially large number of syntactically legitimate ones returned; and undergeneration, or dealing with cases of input outside the systems’ lexical or syntactic coverage. Conventional parsers typically fail to return any useful information when faced with problems of undergeneration or segmentation, and rely on domain-specific detailed semantic information for disambiguation. The problem of text segmentation is best exemplified by sentences (beginning with a capital letter and ending with a period) which—and this sentence is an example—contain text adjuncts delimited by dashes, brackets or commas which may not always stand in a syntactic relation with surrounding material. There has been very limited work on this issue—Hindle (1983a) describes a system which copes with related problems, such as false starts and ‘restarts’ in transcribed spontaneous speech, whilst Jones (1994) and Briscoe & Carroll (1995) describe parsers which make limited use of punctuation to constrain syntactic interpretation. Nevertheless, for exmple, out of the 150K word, balanced Susanne Corpus (Sampson, 1995), over 60% of sentences contain internal punctuation marks and of these around 30% contain text-medial adjuncts. Thus the problem is significant, and further research is required building on linguistic accounts of punctuation (Nunberg, 1990). Disambiguation using knowledge-based techniques requires the specification of too much detailed semantic information to yield a robust domain-independent parser. Yet analysis of the Susanne Corpus with a crude parser suggests that over 80% of sentences are structurally ambiguous. Several parsers yielding a single ‘canonical’ parse have been developed (Marcus, 1980; Hindle, 1983b; de Marcken, 1990). These are often applied to a (partially) disambiguated sequence of lexical syntactic categories. Simplifying the input to the parser in this way circumvents many