Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning. Beam search is used for decoding, and c...

Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies o...

In this paper, we introduce our work on SemEval-2012 task 5: Chinese Semantic Dependency Parsing. Our system is based on MSTParser and two effective methods are proposed: splitting sentence by punctuations and extracting last character of word as lemma. The experiments show that, with a combination of the two proposed methods, our system can improve LAS about one percent and finally get the sec...

We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. The paper reports on a series of experiments using vary...

This study differentiates between probability models that lead to gardenpathing and those that fail to do so in an incremental dependency parser. Models that take into account intermediate parserstates and part-of-speech pairs correctly reflect human preferences in three wellknown cases: Main Verb vs. Reduced Relative ambiguities, Prepositional Phrase Attachment and Subject-Object ambiguities. ...

Today there is a relatively large body of work on automatic acquisition of lexicosyntactical preferences (subcategorization) from corpora. Various techniques have been developed that not only produce machinereadable subcategorization dictionaries but also they are capable of weighing the various subcategorization frames probabilistically. Clearly there should be a potential to use such weighted...

We describe TADPOLE, a modular memory-based morphosyntactic tagger and dependency parser for Dutch. Though primarily aimed at being accurate, the design of the system is also driven by optimizing speed and memory usage, using a trie-based approximation of k-nearest neighbor classification as the basis of each module. We perform an evaluation of its three main modules: a part-of-speech tagger, a...

In this paper, we describe a system for the CoNLL-X shared task of multilingual dependency parsing. It uses a baseline Nivre’s parser (Nivre, 2003) that first identifies the parse actions and then labels the dependency arcs. These two steps are implemented as SVM classifiers using LIBSVM. Features take into account the static context as well as relations dynamically built during parsing. We exp...

We present an empirical study on constructing a Japanese constituent parser, which can output function labels to deal with more detailed syntactic information. Japanese syntactic parse trees are usually represented as unlabeled dependency structure between bunsetsu chunks, however, such expression is insufficient to uncover the syntactic information about distinction between complements and adj...