This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory -- the Syntactic Prediction Locality Theory (SPLT) -- has two components: an integration cost component and a component for the memory cost associated with keeping track of obligatory syntactic requirements. Memory cost is

Approaches to text processing that rely on parsing the text with a context-free grammar tend to be slow and error-prone because of the massive ambiguity of long sentences. In contrast, FASTUS employs a nondeterministic finite-state language model that produces a phrasal decomposition of a sentence into noun groups, verb groups and particles. Another finite-state machine recognizes domain-specific phrases based on combinations of the heads of the constituents found in the first pass. FAS-TUS has been evaluated on several blind tests that demonstrate that state-of-the-art performance on information-extraction tasks is obtainable with surprisingly little computational effort. 1

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding "event structures" are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTU...

FASTUS is a system for extracting information from free text in English, and potentially other languages as well, for entry into a database, and potentially for other applications. It works essentially as a cascaded, nondeterministic finite state automaton. There are four steps in the operation of FASTUS. In Step 1 sentences are scanned for certain trigger words to determine whether further processing should be done. In Step 2 noun groups, verb groups, and prepositions and some other particles are recognized. The input to Step 3 is the sequence of phrases recognized in Step 2; patterns of interest are identified in Step 3 and corresponding “incident structures ” are built up. In Step 4 incident structures that derive from the same incident are identified and merged, and these are used in generating database entries. FASTUS is an order of magnitude faster than any comparable system; it can process a news report in an average of less than eleven seconds. This translates directly into fast development time. In the three and a half weeks between its first use and the MUC-4 evaluation in May 1992, we were able to build up its domain knowledge to a point where it was among the leaders in the evaluation. 1

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton.

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondetermlnlstlc finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding "event structures " are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTUS is very efficient and effective, and has been used successfully in a number of applications.

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding event structures" are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTUS is very effic...

This paper assesses two new approaches to deterministic parsing with respect to the analysis of unbounded dependencies (UDs). UDs in English are highly locally (and often globally) ambiguous. Several researchers have argued that the difficulty of UDs undermines the programme of deterministic parsing. However, their conclusion is based on critiques of various versions of the Marcus parser which represents only one of many possible approaches to deterministic parsing. We examine the predictions made by a LR(1) deterministic parser and the Lexicat deterministic parser concerning the analysis of UDs. The LR(1) technique is powerful enough to resolve the local ambiguities we examine. However, the Lexicat model provides a more psychologically plausible account of the parsing of UDs, which also offers a unified account of the resolution of local and global ambiguities in these constructions.

This paper investigates linguistic performance, from the viewpoint that the information processing in cognitive systems should be designed in terms of constraints rather than procednres in order to deal with partiality of information. In this perspective, the same grammar, the same belief and the same processing architecture should underly both sentence comprehension and production. A basic model of sentence processing, for both comprehension aud production, is derived along tMs line of reasoning. This model is demonstrated to account tbr diverse linguistic phenomena apparently unrelated to each other, lending emplrica! support to the constraint paradigm.

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