Abstract Greedy algorithms for NLP such as transition-based parsing are prone to error propagation. One way overcome this problem is allow the algorithm backtrack and explore an alternative solution in cases where new evidence contradicts explored so far. In order implement a behavior, we use reinforcement learning let action gets better reward than continuing current solution. We test idea on ...

Efficiency is a prime concern in syntactic MT decoding, yet significant developments in statistical parsing with respect to asymptotic efficiency haven’t yet been explored in MT. Recently, McDonald et al. (2005b) formalized dependency parsing as a maximum spanning tree (MST) problem, which can be solved in quadratic time relative to the length of the sentence. They show that MST parsing is almo...

We use an automatic pipeline of word tokenization, stemming, POS tagging, and vocalization to perform real-world Arabic dependency parsing. In spite of the high accuracy on the modules, the very few errors in tokenization, which reaches an accuracy of 99.34%, lead to a drop of more than 10% in parsing, indicating that no high quality dependency parsing of Arabic, and possibly other morphologica...

Previous work on dependency parsing used various kinds of combination models but a systematic analysis and comparison of these approaches is lacking. In this paper we implemented such a study for English dependency parsing and find several non-obvious facts: (a) the diversity of base parsers is more important than complex models for learning (e.g., stacking, supervised meta-classification), (b)...

Dependency parsing has become very popular among researchers from all NLP areas, because dependency representations contain very valuable easy-to-use information. In the last decade a lot of dependency parsers have been developed, each of them somehow special with its own unique characteristics. In the course of this thesis I have developed yet another parser MDParser. In this work I discuss th...

In applications of spoken monologue processing such as simultaneous machine interpretation and real-time captions generation, incremental language parsing is strongly required. This paper proposes a technique for incremental dependency parsing of Japanese spoken monologue on a clause-by-clause basis. The technique identifies the clauses based on clause boundaries analysis, analyzes the dependen...

We address the problem of joint part-of-speech (POS) tagging and dependency parsing in Chinese. In Chinese, some POS tags are often hard to disambiguate without considering longrange syntactic information. Also, the traditional pipeline approach to POS tagging and dependency parsing may suffer from the problem of error propagation. In this paper, we propose the first incremental approach to the...

This paper describes the University of Geneva’s submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is the grandchild of the first transition-based neural network dependency parser, which was the University of Geneva’s entry in the CoNLL 2007 multilingual dependency parsing shared...

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first exten...