We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

This paper describes AUTOSEM, a robust semantic interpretation framework that can operate both at parse time and repair time. The evaluation demon- strates that AUTOSEM achieves a high level of robustness efficiently and without requiring any hand coded knowledge dedicated to repair.

Large practical NLP applications require robust analysis components that can effectively handle input that is disfluent or extra-grammatical. The effectiveness and efficiency of any robust parser are a direct function of three main factors: (1) Flexibility: what types of disfluencies and deviations from the grammar can the parser handle?; (2) Search: How does the parser search the space of possible interpretations, and what techniques are applied to prune the search space?; and (3) Parse Selection and Disambiguation: What methods and resources are used to evaluate and rank potential parses and sub-parses, and how does the parser cope with the extreme levels of ambiguity introduced by its flexibility parameters? In this chapter we describe our investigations on how to balance flexibility and efficiency in the context of two different robust parsers - a GLR parser and a left corner Chart parser - both based on a unification-augmented context-free grammar formalism. We demonstrate how the...

We 1 present a method for automatically extracting a Stochastic Lexicalized Tree Grammar (SLTG) from an HPSG source grammar and a given corpus. Processing of a SLTG is performed by a specialized fast parser. The approach has been tested on a large English grammar and has been shown to achieve a speed-up by a factor of better than 10 compared to parsing with a highly tuned HPSG parser. Our approach is simple and transparent, and comes with no magic tuning strategies. The extracted grammars are declaratively represented and have a high degree of practical applicability. 1 Introduction Head Driven Phrase Structure Grammar (HPSG) has proven to be a quite successful formalism for specifying natural language grammars in a highly modular and compact manner [ Pollard and Sag, 1994 ] supporting the definition of complex linguistic information and interactions between information 1 Neumann was supported by a research grant from the German Federal Ministry of Education, Science, Research and...

Abstract. In this paper I discuss several possible analyses for constituent order in German. Approaches that assume continuous constituents are compared with an approach that assumes discontinuous constituents. I will show that certain proposals that have been made to analyze constituent order are either not adequate or cannot be implemented with currently available systems. For the proposals that can be implemented I will discuss the amount of work a parser has to do. I then compare two implementations of larger fragments of German: the Verbmobil grammar and the Babel grammar. It is shown that the amount of work to be done to parse the Verbmobil grammar is significantly higher than the work that has to be done parsing with the Babel grammar. Key words: German, HPSG, implementation, linearization, parsing 1.

Over the past few years significant progress was accomplished in efficient processing with wide-coverage hpsg grammars. hpsg-based parsing systems are now available that can process medium-complexity sentences (of ten to twenty words, say) in average parse times equivalent to real (i.e. human reading) time. A large number of engineering improvements in current hpsg systems were achieved through collaboration of multiple research centers and mutual exchange of experience, encoding techniques, algorithms, and even pieces of software. This article presents an approach to grammar and system engineering, termed competence & performance profiling, that makes systematic experimentation and the precise empirical study of system properties a focal point in development. Adapting the profiling metaphor familiar from software engineering to constraint-based grammars and parsers, enables developers to maintain an accurate record of system evolution, identify grammar and system deficiencies quickl...

Introduction AUTOSEM is a lexicon driven approach to semantic interpretation built on top of the LCFlex robust parser (Ros'e and Lavie, to appear). Its domain independent knowledge sources include a broad coverage English syntactic parsing grammar and a large scale lexicon built on top of the COMLEX lexicon [Grishman et al., 1994] and containing both word entries as well as idiomatic construction entries. Domain specific semantic knowledge is encoded declaratively within a meaning representation specification. Semantic constructor functions are compiled automatically from this specification and then linked into lexical entries. Thus, the lexicon provides a clean interface between syntactic and semantic knowledge as in the Glue Language Semantics approach [Dalrymple, 1999, Dalrymple et al., 1993]. The grammar allows the parser to assign deep syntactic roles to constituents. Based on these head/argument relationships, the constructor functions enforce semantic se

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Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.

We argue that grammatical processing is a viable alternative to concept spotting for processing spoken input in a practical dialogue system. We discuss the structure of the grammar, the properties of the parser, and a method for achieving robustness. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.