In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English and Chinese data where the base parser could achieve competitive accuracy scores. Our enhanced parser achieved state-of-the-art accuracy on Chinese data and competitive results on English data. We gained a large absolute improvement of one point (UAS) on Chinese and 0.5 points for English.
The first group uses unlabeled data (usually parsed data) directly in the training process as additional training data. The most common approaches in this group are self- co-training. applied first self-training to a constituency parser. This was later adapted to dependency parsing by and . Compared to the self-training approach used by , both self-training approaches for dependency parsing need an additional selection step to predict high-quality parsed sentences for retraining. The basic idea behind this is similar to 's co-training approach. Instead of using a separately trained classifier @cite_3 or confidence-based methods @cite_14 , used two different parsers to obtain the additional training data. shows that when two parsers assign the same syntactic analysis to sentences then the parse trees have usually a higher parsing accuracy. Tri-training @cite_20 @cite_19 is a variant of co-training which involves a third parser. The base parser is retrained on additional parse trees that the other two parsers agreed on.
@cite_19 @cite_14 @cite_20 @cite_3
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We present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions. For example, in the case of English unlabeled second-order parsing, we improve from a baseline accuracy of 92.02 to 93.16 , and in the case of Czech unlabeled second-order parsing, we improve from a baseline accuracy of 86.13 to 87.13 . In addition, we demonstrate that our method also improves performance when small amounts of training data are available, and can roughly halve the amount of supervised data required to reach a desired level of performance. This paper presents a successful approach for domain adaptation of a dependency parser via self-training. We improve parsing accuracy for out-of-domain texts with a self-training approach that uses confidence-based methods to select additional training samples. We compare two confidence-based methods: The first method uses the parse score of the employed parser to measure the confidence into a parse tree. The second method calculates the score differences between the best tree and alternative trees. With these methods, we were able to improve the labeled accuracy score by 1.6 percentage points on texts from a chemical domain and by 0.6 on average on texts of three web domains. Our improvements on the chemical texts of 1.5 UAS is substantially higher than improvements reported in previous work of 0.5 UAS. For the three web domains, no positive results for self-training have been reported before. We present a novel approach for inducing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resourcerich language. We train probabilistic parsing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy regularization. Our method can be used as a purely monolingual dependency parser, requiring no human translations for the test data, thus making it applicable to a wide range of resource-poor languages. We perform experiments on three Data sets — Version 1.0 and version 2.0 of Google Universal Dependency Treebanks and Treebanks from CoNLL shared-tasks, across ten languages. We obtain stateof-the art performance of all the three data sets when compared with previously studied unsupervised and projected parsing systems. Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. In this paper we use selftraining in order to improve the quality of a parser and to adapt it to a different domain, using only small amounts of manually annotated seed data. We report significant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario. In particular, we achieve 50 reduction in annotation cost for the in-domain case, yielding an improvement of 66 over previous work, and a 20-33 reduction for the domain adaptation case. This is the first time that self-training with small labeled datasets is applied successfully to these tasks. We were also able to formulate a characterization of when selftraining is valuable.