To investigate the contributions of taggers or chunkers to the performance of a deep syntactic parser, Weighted Constraint Dependency Grammars have been extended to also take into consideration information from external sources. Using a weak information fusion scheme based on constraint optimization techniques, a parsing accuracy has been achieved which is comparable to other (stochastic) parsers.

This paper describes a pipelined approach for CoNLL-09 shared task on joint learning of syntactic and semantic dependencies. In the system, we handle syntactic dependency parsing with a transition-based approach and utilize MaltParser as the base model. For SRL, we utilize a Maximum Entropy model to identify predicate senses and classify arguments. Experimental results show that the average per...

Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lex...

We describe a parser used in the CoNLL 2006 Shared Task, “Multingual Dependency Parsing.” The parser first identifies syntactic dependencies and then labels those dependencies using a maximum entropy classifier. We consider the impact of feature engineering and the choice of machine learning algorithm, with particular focus on Slovene, Spanish and Swedish.

Information access from clinical text is a research area which has gained a large amount of interest in recent years. Automatic syntactic analysis for the creation of deeper language models is potentially very useful for such methods. However, syntactic parsers that are tailored to accommodate for the distinctive properties of clinical language are rare and costly to build. We present an initia...

In recent years, there has been a considerable amount of interest in using Natural Language Processing in Information Retrieval research, with specific implementations varying from the word-level morphological analysis to syntactic parsing to conceptual-level semantic analysis. In particular, different degrees of phrase-level syntactic information have been incorporated in information retrieval...

The paper presents a Universal Dependencies (UD) annotation scheme for a learner English corpus. The REALEC dataset consists of essays written in English by Russian-speaking university students in the course of general English. The original corpus is manually annotated for learners’ errors and gives information on the error span, error type, and the possible correction of the mistake provided b...

We compare three different approaches to parsing into syntactic, bi-lexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses from the latter two are post-converted to bilexical dependencies. Through this ‘reduction’ of all three approaches to syntactic dependency parsers, we determ...

We implement a Korean syntactic analyzer which decreases many ambiguities in syntax parse trees using segmentation and semantic connection units. We use dependency grammar for parsing. Our syntactic analysis system generates all parse trees of a given sentence. So, the number of parse trees of syntactic analysis is many. To decrease the number of parse trees, we suggested semantic connection un...