Many theories have been proposed to explain difficulty with center embedded constructions, most attributing the problem to some kind of limited capacity short-term memory. However, these theories have developed for the most part independently of more traditional memory research, which has focused on uncovering general principles such as chunking and interference. This article attempts to gain some unification with this research by suggesting that an interesting range of core sentence processing phenomena can be explained as interference effects in a sharply limited syntactic working memory. These include difficult and acceptable embeddings, as well as certain limitations on ambiguity resolution, length effects in garden path structures, and the requirement for locality in syntactic structure. The theory takes the form of an architecture for parsing which can index no more than two constituents under the same syntactic relation. A limitation of two or three items shows up in a variety o...

While there is no interesting limitation on the degree of right-embedding in acceptable sentences, center-embedding is quite severely restricted. Similarly, while there is no interesting bound on the number of nouns that can occur in acceptable noun compounds, there is a very low bound on the number of causative morphemes that can occur in the verb compounds of agglutinative languages. Turning to the clause-final verb clusters of West Germanic languages, we find another similar bound. A cluster including verbs from one embedded clause may beacceptable, but clusters formed from the verbs of two or three or even more deeply embedded clauses are much more awkward (regardless of whether the subject-verb dependencies are crossing or nested). And in languages that allow multiple wh-extractions from a single clause, extractions of more than one element with a given case quickly become unacceptable. More careful experimental study of the nature of these limitations is needed, in a range of languages, but here a preliminary attempt is made to subsume them all under a single generalization, a version of the familar idea that the human parsing

Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combined with treebank-induced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependency-based syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stack-based and list-based algorithms. In the former family, which is restricted to projective dependency structures, we describe an arc-eager and an arc-standard variant; in the latter family, we present a projective and a nonprojective variant. For each of the four algorithms, we give proofs of correctness and complexity. In addition, we perform an experimental evaluation of all algorithms in combination with SVM classifiers for predicting the next parsing action, using data from thirteen languages. We show that all four algorithms give competitive accuracy, although the non-projective list-based algorithm generally outperforms the projective algorithms for languages with a non-negligible proportion of non-projective constructions. However, the projective algorithms often produce comparable results when combined with the technique known as pseudo-projective parsing. The linear time complexity of the stack-based algorithms gives them an advantage with respect to efficiency both in learning and in parsing, but the projective list-based algorithm turns out to be equally efficient in practice. Moreover, when the projective algorithms are used to implement pseudo-projective parsing, they sometimes become less efficient in parsing (but not in learning) than the non-projective list-based algorithm. Although most of the algorithms have been partially described in the literature before, this is the first comprehensive analysis and evaluation of the algorithms within a unified framework. 1.

Deterministic dependency parsing is a robust and efficient approach to syntactic parsing of unrestricted natural language text. In this paper, we analyze its potential for incremental processing and conclude that strict incrementality is not achievable within this framework. However, we also show that it is possible to minimize the number of structures that require nonincremental processing by choosing an optimal parsing algorithm. This claim is substantiated with experimental evidence showing that the algorithm achieves incremental parsing for 68.9% of the input when tested on a random sample of Swedish text. When restricted to sentences that are accepted by the parser, the degree of incrementality increases to 87.9%. 1

It is well known that even extremely linited centerembedding causes people to have difficulty in comprehension, but tits[ left- and right-branching cons[factions produce no such effect. If the difficulty in comprehension is taken to be a result of processing load, as is widely assumed, then measuring the processing load induced by a parsing strategy on these constructions may help determine its plausibility as a psychological model. On this basis, it has been argued [AJ91, JL83] that by identifying processing load with space utilization, we can rule out both top-down and bottom-up parsing as viable candidates for the human sentence processing mechanism, and that left-corner parsing represents a plausible Mternative.

This paper describes how to construct a finite-state machine (FSM) approximating a 'unification-based' grammar using a ]eft-corner grammar transform. The approximation is presented as a series of grammar transforms, and is exact for left-linear and rightlinear CFGs, and for trees up to a user-specified depth of center-embedding.

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ing away from irrelevant details is a theoretical virtue, but the kinds of abstractions that module geography makes can lead to incorrect inferences from data. That such a possibility exists is clearly demonstrated by the working memory research of Just & Carpenter (1992). Briefly, Just and Carpenter have argued that some garden path effects that were previously interpreted in terms of a syntactically encapsulated module can instead be explained by individual differences in working memory capacity. Such an explanation is not considered in a theoretical framework that systematically ignores the role of memory structures in parsing. This point should be taken regardless of whether one is convinced by the current body of empirical support for this particular model---the fact remains that such an explanation could in principle account for the data, and these alternative explanations are only discovered by developing functionally complete architectures. The next few sections describes what ...

This paper presents three self-paced, word-by-word reading experiments that test for the existence of on-line syntactic storage costs in English. To investigate this issue, we compared reading times for sentence regions in which storage costs varied, keeping other factors constant. Experiment 1 manipulated the number of verbs needed to form a grammatical sentence. Experiment 2 investigated whether filler-gap dependencies incur storage costs, and Experiment 3 investigated whether prepositional phrase arguments of verbs incur storage costs. The results of all three experiments demonstrate the role of online storage costs in sentence comprehension. Taken together with other results in the literature, the results also support a theory of sentence comprehension which includes empty categories mediating filler-gap dependencies. 2