226 research outputs found

    Automatic extraction of subcategorization frames for Italian

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    Subcategorization is a kind of knowledge which can be considered as crucial in several NLP tasks, such as Information Extraction or parsing, but the collection of very large resources including subcategorization representation is difficult and time-consuming. Various experiences show that the automatic extraction can be a practical and reliable solution for acquiring such a kind of knowledge. The aim of this paper is at investigating the relationships between subcategorization frame extraction and the nature of data from which the frames have to be extracted, e.g. how much the task can be influenced by the richness/poorness of the annotation. Therefore, we present some experiments that apply statistical subcategorization extraction methods, known in literature, on an Italian treebank that exploits a rich set of dependency relations that can be annotated at different degrees of specificity. Benefiting of the availability of relation sets that implement different granularity in the representation of relations, we evaluate our results with reference to previous works in a cross-linguistic perspective. 1

    Automatic Extraction of Subcategorization from Corpora

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    We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora. Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English. An initial experiment, on a sample of 14 verbs which exhibit multiple complementation patterns, demonstrates that the technique achieves accuracy comparable to previous approaches, which are all limited to a highly restricted set of subcategorization classes. We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount.Comment: 8 pages; requires aclap.sty. To appear in ANLP-9

    D6.1: Technologies and Tools for Lexical Acquisition

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    This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

    Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora

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    In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results

    Using Parallel Texts and Lexicons for Verbal Word Sense Disambiguation

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    We present a system for verbal Word Sense Disambiguation (WSD) that is able to exploit additional information from parallel texts and lexicons. It is an extension of our previous WSD method, which gave promising results but used only monolingual features. In the follow-up work described here, we have explored two additional ideas: using English-Czech bilingual resources (as features only - the task itself remains a monolingual WSD task), and using a 'hybrid' approach, adding features extracted both from a parallel corpus and from manually aligned bilingual valency lexicon entries, which contain subcategorization information. Albeit not all types of features proved useful, both ideas and additions have led to significant improvements for both languages explored

    An Experiment in Verb Valency Frame Extraction from Croatian Dependency Treebank

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    The paper presents an approach to semi-automatic verb valency frame extraction from the Croatian Dependency Treebank. Our algorithm extracted 1923 verb valency frames for 594 different verbs. We discuss applicability of our method to semi-automatic verb valency lexicon creation and refinement, along with possibilities of utilizing it in the task of parsing Croatian texts

    Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations

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    International audienceAn important trend in recent works on lexical semantics has been the development of learning methods capable of extracting semantic information from text corpora. The majority of these methods are based on the distributional hypothesis of meaning and acquire semantic information by identifying distributional patterns in texts. In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Français). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy
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