Alexandre Agustini

Clustering Syntactic Positions with Similar Semantic Requirements

This article describes an unsupervised strategy to acquire syntactico-semantic requirements of nouns, verbs, and adjectives from partially parsed text corpora. The linguistic notion of requirement underlying this strategy is based on two specific assumptions. First, it is assumed that two words in a dependency are mutually required. This phenomenon is called here corequirement. Second, it is also claimed that the set of words occurring in similar positions defines extensionally the requirements associated with these positions. The main aim of the learning strategy presented in this article is to identify clusters of similar positions by identifying the words that define their requirements extensionally. This strategy allows us to learn the syntactic and semantic requirements of words in different positions. This information is used to solve attachment ambiguities. Results of this particular task are evaluated at the end of the article. Extensive experimentation was performed on Portuguese text corpora.

Syntactic-Based Methods for Measuring Word Similarity

This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semantic information available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against which words are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.

Selection Restrictions Acquisition from Corpora

This paper describes an automatic clustering strategy for acquiring selection restrictions. We use a knowledge-poor method merely based on word cooccurrence within basic syntactic constructions; hence, neither semantic tagged corpora nor man-made lexical resources are needed for generalising semantic restrictions. Our strategy relies on two basic linguistic assumptions. First, we assume that two syntactically related words impose semantic selectional restrictions to each other (cospecification). Second, it is also claimed that two syntactic contexts impose the same selection restrictions if they cooccur with the same words (contextual hypothesis). In order to test our learning method, preliminary experiments have been performed on a Portuguese corpus.

Inducing classes of terms from text

This paper describes a clustering method for organizing in semantic classes a list of terms. The experiments were made using a POS annotated corpus, the ACL Anthology, which consists of technical articles in the field of Computational Linguistics. The method, mainly based on some assumptions of Formal Concept Analysis, consists in building bi-dimensional clusters of both terms and their lexico-syntactic contexts. Each generated cluster is defined as a semantic class with a set of terms describing the extension of the class and a set of contexts perceived as the intensional attributes (or properties) valid for all the terms in the extension. The clustering process relies on two restrictive operations: abstraction and specification. The result is a concept lattice that describes a domain-specific ontology of terms.

Selection restrictions acquisition for parsing improvement

Partially parsed corpora is used for automatically extracting semantic and syntactic subcategorization information for words, helping to cluster them according to their sense which is highly restricted by the syntactic contexts where words do occur. In this paper we propose the use of a parsing platform, based on chart parsing and tabling, in order to check if the syntactic and semantic information extracted automatically leads to better parses than assuming that words do not subcategorize anything.

Acquiring Semantic Classes to Elaborate Attachment Heuristics

We use an unsupervised method for learning word classes from partially parsed text corpora. A word class consists of those words that can appear in similar contexts of subcategorization. The main objective of this paper will be to describe an evaluation procedure based on attachment resolution. We will evaluate whether the classes that have been previously learnt are useful in that syntactic task.

Assessment of Selection Restrictions Acquisition

This paper describes an automatic clustering strategy for acquiring both syntactic and semantic subcategorization restrictions from corpora. In order to test our method, preliminary experiments have been performed on a law-case Portuguese corpus. The acquired information is then used for lexicon upgrading and it is validated by a parsing diagnosis system.