Dependency parsing has attracted considerable interest from researchers and developers in natural language processing. However, to obtain a high‐accuracy dependency parser, supervised techniques require a large volume of hand‐annotated data, which are extremely expensive. This paper presents a simple and effective approach for improving dependency parsing with subtrees derived from unannotated data, which are easy to obtain. First, we use a baseline parser to parse large‐scale unannotated data. Then, we extract subtrees from dependency parse trees in the auto‐parsed data. Next, the extracted subtrees are classified into several sets according to their frequency. Finally, we design new features based on the subtree sets for parsing algorithms. To demonstrate the effectiveness of our proposed approach, we conduct experiments on the English Penn Treebank and Chinese Penn Treebank. The results show that our approach significantly outperforms baseline systems. It also achieves the best accuracy for the Chinese data and an accuracy competitive with the best known systems for the English data.

This paper proposes a method to improve the accuracy of bilingual texts (bitexts) dependency parsing by using an auto-generated bilingual treebank created with the help of statistical machine translation (SMT) systems. Previous bitext parsing methods use human-annotated bilingual treebanks that are costly and troublesome to obtain. In the proposed method, we use an auto-generated bilingual treebank to train the parsing models. First, an SMT system is used to translate a monolingual treebank into the target language; then, a monolingual parser for the target language is used to parse the translated sentences. Since the auto-translated sentences and auto-parsed trees in the auto-generated bilingual treebank are far from perfect, the bilingual constraints are not sufficiently reliable. To overcome this problem, we propose a method to verify the reliability of the constraints using a large amount of target monolingual and bilingual unannotated data. Finally, we design a set of effective bilingual features for parsing models on the basis of the verified constraints. We conduct the experiments using a standard test data. The experimental results show that our bitext parser significantly outperforms monolingual parsers. Moreover, our method is still able to provide improvement when we use a larger monolingual treebank containing over 50 000 sentences. We also test the proposed method with different SMT systems and the results show that our method is very robust to the noise. In particular, the proposed method can be used in a purely monolingual setting with the help of SMT. That is, it does not need the human translation of the test set as previous methods do.

This paper proposes a minimally supervised method for acquiring high-level semantic relations such as causality and prevention from the Web. Our method learns linguistic patterns that express causality such as “x gave rise to y”, and uses them to extract causal noun pairs like (global warming, malaria epidemic) from sentences like “global warming gave rise to a new malaria epidemic”. The novelty of our method lies in the use of semantic word classes acquired by large scale clustering for learning class dependent patterns. We demonstrate the effectiveness of this class based approach on three large-scale relation mining tasks from 50 million Japanese Web pages. In two of these tasks we obtained more than 30,000 relation instances with over 80% precision, outperforming a state-of-the-art system by a large margin.

We present a novel method for discovering missing cross-language links between English and Japanese Wikipedia articles. We collect candidates of missing cross-language links -- a pair of English and Japanese Wikipedia articles, which could be connected by cross-language links. Then we select the correct cross-language links among the candidates by using a classifier trained with various types of features. Our method has three desirable characteristics for discovering missing links. First, our method can discover cross-language links with high accuracy (92\% precision with 78\% recall rates). Second, the features used in a classifier are language-independent. Third, without relying on any external knowledge, we generate the features based on resources automatically obtained from Wikipedia. In this work, we discover approximately $10^5$ missing cross-language links from Wikipedia, which are almost two-thirds as many as the existing cross-language links in Wikipedia.