Acquisition Of Subcategorization Frames Research Articles

Learning argument/adjunct distinction for Basque

This paper presents experiments performed on lexical knowledge acquisition in the form of verbal argumental information. The system obtains the data from raw corpora after the application of a partial parser and statistical filters. We used two different statistical filters to acquire the argumental information: Mutual Information, and Fisher's Exact test. Due to the characteristics of agglutinative languages like Basque, the usual classification of arguments in terms of their syntactic category (such as NP or PP) is not suitable. For that reason, the arguments will be classified in 48 different kinds of case markers, which makes the system fine grained if compared to equivalent systems developed for other languages. This work addresses the problem of learning subcategorization frames by distinguishing arguments from adjuncts, being the last ones the most significant source of noise in subcategorization frame acquisition.

Semi-automatic Generation of Subcategorization Frames for Spanish Verbs Using Ontologies and Verbs Functional Class

This work deals with the semi-automatic generation of subcategorization frames (SCFs) of Spanish verbs; specifically, given a set of verbs in Spanish and their respective sense, their SCFs are obtained. The acquisition of SCFs in Spanish has been approached in different works: in some the frames are generated manually, while in others they are obtained semi-automatically from a tagged corpus; unfortunately in this case, the results depend on the characteristics of the texts used. The method proposed in this document combines an ontology-based approach (through lexical relations of verbs) and linguistic knowledge (functional class of verbs). The relations among base verbs and other verbs were obtained from the Spanish WordNet ontology, which contains lexical relations among words. Also, the existing relation between the SCF and the functional class of verbs was used to generate the SCFs. In order to evaluate the method, the SCFs for 44 base verbs were generated manually, from which 239 SCFs were automatically generated and validated, yielding an accuracy of 89.38%.

Acquisition of verb categories

The paper was delivered as a commentary upon Michael Brent's presentation ‘Acquisition of subcategorization frames using aggregated evidence from local syntactic cues’ to the Penn Language Acquisition Conference, IRCS, University of Pennsylvania, 1992. It argues in support of using statistical techniques like Brent's to minimise the consequences of errors and misanalyses, but concludes that the case for believing that children acquire subcategorisation and other aspects of syntax on the basis of semantic and contextual cues remains strong.

Surface cues and robust inference as a basis for the early acquisition of subcategorization frames

How could children possibly acquire their first subcategorization frames? The hypothesis that they directly observe the syntactic structures of the utterances they hear raises two questions. First, how can children parse input utterances reliably without already knowing the syntactic properties of all the words in them? Second, how do children survive the ungrammatical or misconstrued utterances they inevitably encounter? This paper suggests a specific inference algorithm that substantially reduces the effects of ungrammatical or misconstrued input. Since children must have some such inference procedure, they can use approximate cues to determine syntactic structure. In particular, they can use string-local surface cues rather than global constraints. Such cues make it possible to discover relevant syntactic structure in an utterance without already knowing all the words in it. This paper suggests a possible set of cues for English subcategorization frames that assumes only the ability to detect the ends of utterances and knowledge of a few function morphemes and proper names. Simulation experiments on naturally occuring, child-directed English show that these cues, combined with the proposed inference mechanism, do surprisingly well at discovering subcategorization frames.