We describe a stochastic approach to partial parsing, i.e., the recognition of syntactic structures of limited depth. The technique utilises Markov Models, but goes beyond usual bracketing approaches, since it is capable of recognising not only the boundaries, but also the internal structure and syntactic category of simple as well as complex NP's, PP's, AP's and adverbials. We compare tagging accuracy for different applications and encoding schemes.
Symbolic NP chunkers usually rely on finite automata and or pattern matching, cf. @cite_0 , @cite_2 . presents a partial parsing technique based on cascaded finite automata. describe a POS tagger and shallow parser combining symbolic and stochastic processing via relaxation labelling .
@cite_0 @cite_2
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For the cascaded task of Chinese word segmentation, POS tagging and parsing, the pipeline approach suffers from error propagation while the joint learning approach suffers from inefficient decoding due to the large combined search space. In this paper, we present a novel lattice-based framework in which a Chinese sentence is first segmented into a word lattice, and then a lattice-based POS tagger and a lattice-based parser are used to process the lattice from two different viewpoints: sequential POS tagging and hierarchical tree building. A strategy is designed to exploit the complementary strengths of the tagger and parser, and encourage them to predict agreed structures. Experimental results on Chinese Treebank show that our lattice-based framework significantly improves the accuracy of the three sub-tasks. We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to part-of-speech (POS) tagging. The algorithm uses a similarity graph to encourage similar n-grams to have similar POS tags. We demonstrate the efficacy of our approach on a domain adaptation task, where we assume that we have access to large amounts of unlabeled data from the target domain, but no additional labeled data. The similarity graph is used during training to smooth the state posteriors on the target domain. Standard inference can be used at test time. Our approach is able to scale to very large problems and yields significantly improved target domain accuracy.