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
1716844867 2250737685 2133556223 2396955895
(2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLL-X Shared Task datasets. The paper shows how triads of stacked dependency parsers described in (2008) can label unlabeled data for each other in a way similar to co-training and produce end parsers that are significantly better than any of the stacked input parsers. We evaluate our system on five datasets from the CONLL-X Shared Task and obtain 10--20 error reductions, incl. the best reported results on four of them. We compare our approach to other semi-supervised learning algorithms. 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. In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. In this paper, a new co-training style semi-supervised learning algorithm, named tri-training, is proposed. This algorithm generates three classifiers from the original labeled example set. These classifiers are then refined using unlabeled examples in the tri-training process. In detail, in each round of tri-training, an unlabeled example is labeled for a classifier if the other two classifiers agree on the labeling, under certain conditions. Since tri-training neither requires the instance space to be described with sufficient and redundant views nor does it put any constraints on the supervised learning algorithm, its applicability is broader than that of previous co-training style algorithms. Experiments on UCI data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance. The accuracy of parsing has exceeded 90 recently, but this is not high enough to use parsing results practically in natural language processing (NLP) applications such as paraphrase acquisition and relation extraction. We present a method for detecting reliable parses out of the outputs of a single dependency parser. This technique is also applied to domain adaptation of dependency parsing. Our goal was to improve the performance of a state-of-the-art dependency parser on the data set of the domain adaptation track of the CoNLL 2007 shared task, a formidable challenge.