Statistical parsing of morphologically rich languages (SPMRL): what, how and whither

The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations

The processing of ambiguous sentences by first and second language learners of English

This study compares the way English-speaking children and adult second language learners of English resolve relative clause attachment ambiguities in sentences such as The dean liked the secretary of the professor who was reading a letter. Two groups of advanced L2 learners of English with Greek or German as their L1 participated in a set of off-line and on-line tasks. While the participants ' disambiguation preferences were influenced by lexical-semantic properties of the preposition linking the two potential antecedent NPs (of vs. with), there was no evidence that they were applying any structure-based ambiguity resolution strategies of the type that have been claimed to influence sentence processing in monolingual adults. These findings differ markedly from those obtained from 6 to 7 yearold monolingual English children in a parallel auditory study (Felser, Marinis, & Clahsen, submitted) in that the children's attachment preferences were not affected by the type of preposition at all. We argue that whereas children primarily rely on structure-based parsing principles during processing, adult L2 learners are guided mainly by non-structural informatio

Evaluating two methods for Treebank grammar compaction

Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. In this paper, we explore ways by which a treebank grammar can be reduced in size or ‘compacted’, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision