Dependency Parsing: Past, Present, and Future

Dependency parsing has gained more and more interest in natural language processing in recent years due to its simplicity and general applicability for diverse languages. The international conference of computational natural language learning (CoNLL) has organized shared tasks on multilingual dependency parsing successively from 2006 to 2009, which leads to extensive progress on dependency parsing in both theoretical and practical perspectives. Meanwhile, dependency parsing has been successfully applied to machine translation, question answering, text mining, etc. To date, research on dependency parsing mainly focuses on data-driven supervised approaches and results show that the supervised models can achieve reasonable performance on in-domain texts for a variety of languages when manually labeled data is provided. However, relatively less effort is devoted to parsing out-domain texts and resource-poor languages, and few successful techniques are bought up for such scenario. This tutorial will cover all these research topics of dependency parsing and is composed of four major parts. Especially, we will survey the present progress of semi-supervised dependency parsing, web data parsing, and multilingual text parsing, and show some directions for future work. In the first part, we will introduce the fundamentals and supervised approaches for dependency parsing. The fundamentals include examples of dependency trees, annotated treebanks, evaluation metrics, and comparisons with other syntactic formulations like constituent parsing. Then we will introduce a few mainstream supervised approaches, i.e., transition-based, graph-based, easy-first, constituent-based dependency parsing. These approaches study dependency parsing from different perspectives, and achieve comparable and state-of-the-art performance for a wide range of languages. Then we will move to the hybrid models that combine the advantages of the above approaches. We will also introduce recent work on efficient parsing techniques, joint lexical analysis and dependency parsing, multiple treebank exploitation, etc. In the second part, we will survey the work on semi-supervised dependency parsing techniques. Such work aims to explore unlabeled data so that the parser can achieve higher performance. This tutorial will present several successful techniques that utilize information from different levels: whole tree level, partial tree level, and lexical level. We will discuss the advantages and limitations of these existing techniques. In the third part, we will survey the work on dependency parsing techniques for domain adaptation and web data. To advance research on out-domain parsing, researchers have organized two shared tasks, i.e., the CoNLL 2007 shared task and the shared task of syntactic analysis of non-canonical languages (SANCL 2012). Both two shared tasks attracted many participants. These participants tried different techniques to adapt the parser trained on WSJ texts to out-domain texts with the help of large-scale unlabeled data. Especially, we will present a brief survey on text normalization, which is proven to be very useful for parsing web data. In the fourth part, we will introduce the recent work on exploiting multilingual texts for dependency parsing, which falls into two lines of research. The first line is to improve supervised dependency parser with multilingual texts. The intuition behind is that ambiguities in the target language may be unambiguous in the source language. The other line is multilingual transfer learning which aims to project the syntactic knowledge from the source language to the target language.