Using Computers in Linguistics: A Practical Guide

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Show Less Restricted access. This book presents a richly illustrated, hands-on discussion of one of the fastest growing fields in linguistics today. The authors address key methodological issues in corpus linguistics, such as collocations, keywords and the categorization of concordance lines. They show how these topics can be explored step-by-step with BNCweb , a user-friendly web-based tool that supports sophisticated analyses of the million-word British National Corpus.

For example, in the sentence of figure 1 we would treat Thetis and mortal as dependents of loves , using dependency links labeled subj and obj respectively, and the determiner a would in turn be a dependent of mortal , via a dependency link mod for modifier. Projective dependency grammars are ones with no crossing dependencies so that the descendants of a node form a continuous text segment , and these generate the same languages as CFGs.

Significantly, mildly non-projective dependency grammars, allowing a head word to dominate two separated blocks, provide the same generative capacity as the previously mentioned mildly context-sensitive frameworks that are needed for some languages Kuhlmann As noted at the beginning of this section, traditional formal grammars proved too limited in coverage and too rigid in their grammaticality criteria to provide a basis for robust coverage of natural languages as actually used, and this situation persisted until the advent of probabilistic grammars derived from sizable phrase-bracketed corpora notably the Penn Treebank.

The simplest example of this type of grammar is a probabilistic context-free grammar or PCFG.

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At the lowest level, the expansion probabilities specify how frequently a given part of speech such as Det, N, or V will be realized as a particular word. Such a grammar provides not only a structural but also a distributional model of language, predicting the frequency of occurrence of various phrase sequences and, at the lowest level, word sequences. However, the simplest models of this type do not model the statistics of actual language corpora very accurately, because the expansion probabilities for a given phrase type or part of speech X ignore the surrounding phrasal context and the more detailed properties such as head words of the generated constituents.

Such modeling inaccuracies lead to parsing inaccuracies see next subsection , and therefore generative grammar models have been refined in various ways, for example in so-called lexicalized models allowing for specification of particular phrasal head words in rules, or in tree substitution grammars allowing expansion of nonterminals into subtrees of depth 2 or more.

Nevertheless, it seems likely that fully accurate distributional modeling of language would need to take account of semantic content, discourse structure, and intentions in communication, not only of phrase structure. Possibly construction grammars e. Natural language analysis in the early days of AI tended to rely on template matching, for example, matching templates such as X has Y or how many Y are there on X to the input to be analyzed. This of course depended on having a very restricted discourse and task domain.

By the late s and early 70s, quite sophisticated recursive parsing techniques were being employed. For example, Woods' lunar system used a top-down recursive parsing strategy interpreting an ATN in the manner roughly indicated in section 2. It also saved recognized constituents in a table, much like the class of parsers we are about to describe.


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  7. The latter algorithm, termed the CYK or CKY algorithm for the three separate authors, was particularly simple, using a bottom-up dynamic programming approach to first identify and tabulate the possible types nonterminal labels of sentence segments of length 1 i. This process runs in cubic time in the length of the sentence, and a parse tree can be constructed from the tabulated constituents in quadratic time. The method most frequently employed nowadays in fully analyzing sentential structure is chart parsing.

    This is a conceptually simple and efficient dynamic programming method closely related to the algorithms just mentioned; i. There are many variants, depending on whether only complete constituents are posited or incomplete ones as well to be progressively extended , and whether we proceed left-to-right through the word stream or in some other order e. Newly completed constituents are placed on an agenda , and items are successively taken off the agenda and used if possible as left corners of new, higher-level constituents, and to extend partially completed constituents.

    The chart is used both to avoid duplication of constituents already built, and ultimately to reconstruct one or more global structural analyses. If all possible chart entries are built, the final chart will allow reconstruction of all possible parses. Chart-parsing methods carry over to PCFGs essentially without change, still running within a cubic time bound in terms of sentence length. An extra task is maintaining probabilities of completed chart entries and perhaps bounds on probabilities of incomplete entries, for pruning purposes.

    However, it does not follow that TAG parsing or CCG parsing is impractical for real grammars and real language, and in fact parsers exist for both that are competitive with more common CFG-based parsers.