This paper presents a new parsing method using statistical information extracted from corpus, especially for Korean. The structural ambiguities are occurred in deciding the dependency relation between words in Korean. While guring out the correct dependency, the lexical associations play an important role in resolving the ambiguities. Our parser uses statistical cooccurrence data to compute the...

This paper describes practical issues in the framework-independent evaluation of deep and shallow parsers. We focus on the use of two dependencybased syntactic representation formats in parser evaluation, namely, Carroll et al. (1998)’s Grammatical Relations and de Marneffe et al. (2006)’s Stanford Dependency scheme. Our approach is to convert the output of parsers into these two formats, and m...

In this paper we focus on practical issues of data representation for dependency parsing. We carry out an experimental comparison of (a) three syntactic dependency schemes; (b) three data-driven dependency parsers; and (c) the influence of two different approaches to lexical category disambiguation (aka tagging) prior to parsing. Comparing parsing accuracies in various setups, we study the inte...

In this paper, we propose a new language model, namely, a dependency structure language model, for information retrieval to compensate for the weakness of bigram and trigram language models. The dependency structure language model is based on the Chow Expansion theory and the dependency parse tree generated by a dependency parser. So, long-distance dependencies can be handled by the dependency ...

In the paper we try to show that a lazy functional language such as Haskell is a convenient framework not only for implementing dependency parsers but also for expressing dependency grammars directly in the programming language in a compact, readable and mathematically clean way. The parser core, supplying necessary types and functions, is presented together with two examples of grammars: one t...

CDG represents a sentence’s grammatical structure as assignments of dependency relations to functional variables associated with each word in the sentence. In this paper, we describe a statistical CDG (SCDG) parser that performs parsing incrementally and evaluate it on the Wall Street Journal Penn Treebank. Using a tight integration of multiple knowledge sources, together with distance modeling...

Clause boundary identification is a very important task in natural language processing. Identifying the clauses in the sentence becomes a tough task if the clauses are embedded inside other clauses in the sentence. In our approach, we use the dependency parser to identify the boundary for the clause. The dependency tag set, contains 11 tags, and is useful for identifying the boundary of the cla...

This paper presents a Chinese dependency syntax for treebanking. The syntax contains 13 word classes and 34 dependency types. A format of treebank based on the syntax is also proposed for the applications of computational and general linguistic research. Some experiments show that the treebank based on the proposed dependency syntax can be used for training and evaluating the dependency parser ...

One of the main motivations for building treebanks is that they facilitate the development of syntactic parsers, by providing realistic data for evaluation as well as inductive learning. In this paper we present what we believe to be the first robust data-driven parser for Bulgarian, trained and evaluated on data from BulTreeBank (Simov et al., 2002). The parser uses dependency-based representa...

– In this paper, we propose a new language model, namely, a dependency structure language model, for information retrieval to compensate for the weakness of bigram and trigram language models. The dependency structure language model is based on the Chow Expansion theory and the dependency parse tree generated by a dependency parser. So, longdistance dependencies can be handled by the dependency...