MaltParser: A language-independent system for data-driven dependency parsing

Abstract

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

Building the essential resources for Finnish: the Turku Dependency Treebank

“…With the conversion included in the original Stanford tools, 4 the Penn Treebank (Marcus et al 1993) and indeed any treebank annotated in the Penn Treebank constituency scheme can be converted into the SD scheme. In addition, the SD scheme is especially popular in parser evaluation works (Cer et al 2010;Nivre et al 2010;Clegg and Shepherd 2007;Miwa et al 2010;Foster et al 2011), and several parsers are capable of producing the scheme either natively or by conversion, including the Charniak-Johnson parser (Charniak and Johnson 2005), the Stanford parser (Klein and Manning 2003), the Clear parser Choi and Palmer (2011), the parser of Tratz and Hovy (2011), and naturally any dependency parser that can be trained from a treebank, such as the MaltParser (Nivre et al 2007), the MSTParser (McDonald et al 2006) or the MateTools parser (Bohnet 2010). The scheme was originally intended to be applicationoriented, and it has indeed been successfully used in applications, particularly in the biomedical domain (Björne et al 2010;Miyao et al 2009;Qian and Zhou 2012), and otherwise in opinion extraction (Zhuang et al 2006) and sentiment analysis (Meena and Prabhakar 2007).…”

The Utility of Discourse Structure in Forum Thread Retrieval

“…We used the same features as [2], but different parsing approaches. Specifically, we approached thread discourse structure parsing as a joint link and dialogue act classification task, using conditional random fields [9] and dependency parsing [10]. We also demonstrated that our discourse structure parsing method was able to perform equally well over partial threads as complete threads, by experimenting with "in situ" classification of evolving threads.…”

“…The results are evaluated using post-level micro-averaged F-score (β = 1). All three discourse parsing methods described above were tested in our experiments, using CRFSGD [25] and MaltParser [10]. For features, we experimented with all the features proposed in our earlier work [3], as well as many of our own features.…”

Automatic Identification of Chinese Prepositional Phrase Including Verbs

“…The implementation tool in this paper is Dependency parser MaltParser [13], which has the feature as Data driven, trainable, uncertainty. In order to predict the activities of the parser and select the appropriate parsing, it uses a studying strategy based on memory-and-SVM.…”