Showing papers on "Chunking (computing) published in 2002"

Japanese dependency analysis using cascaded chunking

Abstract: In this paper, we propose a new statistical Japanese dependency parser using a cascaded chunking model. Conventional Japanese statistical dependency parsers are mainly based on a probabilistic model, which is not always efficient or scalable. We propose a new method that is simple and efficient, since it parses a sentence deterministically only deciding whether the current segment modifies the segment on its immediate right hand side. Experiments using the Kyoto University Corpus show that the method outperforms previous systems as well as improves the parsing and training efficiency.

Shallow parsing and text chunking: a view on underspecification in syntax

Abstract: This paper illustrates a technique of shallow parsing named “text chunking” whereby “parse incompleteness” is reinterpreted as “parse underspecification”. A text is chunked into structured units which can be identified with certainty on the basis of available knowledge. The chunking process stops at that level of granularity beyond which the analysis gets undecidable. We argue that a chunked syntactic representation can usefully be exploited as such for non trivial NLP applications which do not require full text understanding such as automatic lexical acquisition and information retrieval.

Shallow language processing architecture for Bulgarian

Abstract: This paper describes LINGUA - an architecture for text processing in Bulgarian. First, the pre-processing modules for tokenisation, sentence splitting, paragraph segmentation, part-of-speech tagging, clause chunking and noun phrase extraction are outlined. Next, the paper proceeds to describe in more detail the anaphora resolution module. Evaluation results are reported for each processing task.

Structure alignment using bilingual chunking

Abstract: A new statistical method called "bilingual chunking" for structure alignment is proposed. Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks. The alignment is finished through a simultaneous bilingual chunking algorithm. Using the constrains of chunk correspondence between source language (SL) and target language (TL), our algorithm can dramatically reduce search space, support time synchronous DP algorithm, and lead to highly consistent chunking. Furthermore, by unifying the POS tagging and chunking in the search process, our algorithm alleviates effectively the influence of POS tagging deficiency to the chunking result.The experimental results with English-Chinese structure alignment show that our model can produce 90% in precision for chunking, and 87% in precision for chunk alignment.

Baitfish chunking apparatus

Abstract: Apparatus for cutting baitfish employing a non-symmetrically arcuate biasing member for angularly articulating a baitfish against a plurality of blade surfaces. In certain embodiments, apparatus having an improved lever arm with increased mechanical advantage.

Chinese base-phrases chunking

Abstract: This paper introduces new definitions of Chinese base phrases and presents a hybrid model to combine Memory-Based Learning method and disambiguation proposal based on lexical information and grammar rules populated from a large corpus for 9 types of Chinese base phrases chunking Our experiment achieves an accuracy (F-measure) of 934% The significance of the research lies in the fact that it provides a solid foundation for the Chinese parser

Experiments in German noun chunking

Abstract: The paper describes a method to process recursive noun phrases with finite-state cascades. It is shown that chunking of recursive noun phrases necessitates a readjustment of the finite-state cascades approach. In particular, the property of monotonicity must be given up. Furthermore, the paper explores the influence of POS tags and online agreement checking on the overall performance.

Text chunking based on a generalization of winnow

Abstract: This paper describes a text chunking system based on a generalization of the Winnow algorithm. We propose a general statistical model for text chunking which we then convert into a classification p...