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.
In section , we sketched the simple bracketing technique described by , which provided motivation for our chunking method. As far as other approaches are concerned, our work is most closely related to that of , who use Markov Models in a preprocessing step to reduce the number of tree segments (called supertags ) that can be assigned to a word in a lexicalised Tree Adjoining Grammar. This approach makes parsing more efficient, but it needs a large training corpus, has to fight a large amount of ambiguity and needs a subsequent parsing step (also see @cite_1 for the use of explanation-based learning for this purpose).
@cite_1
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In this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. The supertags are designed such that only those elements on which the lexical item imposes constraints appear within a given supertag. Further, each lexical item is associated with as many supertags as the number of different syntactic contexts in which the lexical item can appear. This makes the number of different descriptions for each lexical item much larger than when the descriptions are less complex, thus increasing the local ambiguity for a parser. But this local ambiguity can be resolved by using statistical distributions of supertag co-occurrences collected from a corpus of parses. We have explored these ideas in the context of the Lexicalized Tree-Adjoining Grammar (LTAG) framework. The supertags in LTAG combine both phrase structure information and dependency information in a single representation. Supertag disambiguation results in a representation that is effectively a parse (an almost parse), and the parser need "only" combine the individual supertags. This method of parsing can also be used to parse sentence fragments such as in spoken utterances where the disambiguated supertag sequence may not combine into a single structure. This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 F-measure, a 13 relative decrease in F measure error over the baseline model's score of 88.2 . The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within log-linear (maximum-entropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. This paper presents the IMS contribution to the CoNLL 2017 Shared Task. In the preprocessing step we employed a CRFPOS morphological tagger and a neural tagger predicting supertags. On some languages, we also applied word segmenta- tion with the CRF tagger and sentence segmentation with a perceptron-based parser. For parsing we took an ensemble approach by blending multiple instances of three parsers with very different architectures. Our system achieved the third place overall and the second place for the surprise languages In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which had been tagged by a human annotator to train the model. More recently, (1992) suggest that training can be achieved with a minimal lexicon and a limited amount of a priori information about probabilities, by using an Baum-Welch re-estimation to automatically refine the model. In this paper, I report two experiments designed to determine how much manual training information is needed. The first experiment suggests that initial biasing of either lexical or transition probabilities is essential to achieve a good accuracy. The second experiment reveals that there are three distinct patterns of Baum-Welch reestimation. In two of the patterns, the re-estimation ultimately reduces the accuracy of the tagging rather than improving it. The pattern which is applicable can be predicted from the quality of the initial model and the similarity between the tagged training corpus (if any) and the corpus to be tagged. Heuristics for deciding how to use re-estimation in an effective manner are given. The conclusions are broadly in agreement with those of Merialdo (1994), but give greater detail about the contributions of different parts of the model.