Mansuk Song

A corpus-based approach for Korean nominal compound analysis based on linguistic and statistical information

The syntactic structure of a nominal compound must be analyzed first for its semantic interpretation. In addition, the syntactic analysis of nominal compounds is very useful for NLP application such as information extraction, since a nominal compound often has a similar linguistic structure with a simple sentence, as well as representing concrete and compound meaning of an object with several nouns combined. In this paper, we present a novel model for structural analysis of nominal compounds using linguistic and statistical knowledge which is coupled based on lexical information. That is, the syntactic relations defined between nouns (complement-predicate and modifier-head relation) are obtained from large corpora and again used to analyze the structures of nominal compounds and identify the underlying relations between nouns. Experiments show that the model gives good results, and can be effectively used for application systems which do not require deep semantic information.

Automatic extraction of collocations from Korean text

In this paper, we propose a statistical method to automaticallyextract collocations from Korean POS-tagged corpus. Since a large portion of language is represented by collocation patterns, the collocational knowledge provides a valuable resource for NLP applications. One difficulty of collocation extraction is that Korean has a partially free word order, which also appears in collocations. In this work, we exploit four statistics, ‘frequency’,‘randomness’, ‘convergence’, and ‘correlation in order to take into account the flexible word order of Korean collocations. We separate meaningful bigrams using an evaluation function based on the four statistics and extend the bigrams to n-gram collocations using a fuzzy relation. Experiments show that this method works well for Korean collocations.

Symmetric quadrature formulas over a unit disk

An algorithm to get an optimal choice for the number of symmetric quadrature points is given to find symmetric quadrature formulas over a unit disk with a minimal number of points even when a high degree of polynomial precision is required. The symmetric quadrature formulas for numerical integration over a unit disk of complete polynomial functions up to degree 19 are presented.

Structural feature selection for English-Korean statistical machine translation

When aligning texts in very different languages such as Korean and English, structural features beyond word or phrase give useful information. In this paper, we present a method for selecting structural features of two languages, from which we construct a model that assigns the conditional probabilities to corresponding tag sequences in bilingual English-Korean corpora. For tag sequence mapping between two languages, we first define a structural feature function which represents statistical properties of empirical distribution of a set of training samples. The system, based on maximum entropy concept, selects only features that produce high increases in loglikelihood of training samples. These structurally mapped features are more informative knowledge for statistical machine translation between English and Korean. Also, the information can help to reduce the parameter space of statistical alignment by eliminating syntactically unlikely alignments.

Identifying temporal expression and its syntactic role using FST and lexical data from corpus

Accurate analysis of the temporal expression is crucial for Korean text processing applications such as information extraction and chunking for efficient syntactic analysis. It is a complicated problem since temporal expressions often have the ambiguity of syntactic roles. This paper discusses two problems: (1) representing and identifying the temporal expression (2) distinguishing the syntactic function of the temporal expression in case it has a dual syntactic role. In this paper, temporal expressions and the context for disambiguation which is called local context are represented using lexical data extracted from corpus and the finite state transducer. By experiments, it turns out that the method is effective for temporal expression analysis. In particular, our approach shows the corpus-based work could make a promising result for the problem in a restricted domain in that we can effectievely deal with a large size of lexical data.

A New Parsing Method Using a Global Association Table

This chapter presents a new parsing method using statistical information extracted from a corpus, especially for Korean. In Korean, structural ambiguities occur in the dependency relations between words. While figuring out the correct dependency, lexical associations play an important role. Our parser uses statistical co-occurrence data to compute lexical associations. We show that sentences can be parsed deterministically by means of global management of the associations. The global association table (GAT) is defined, and we describe how the associations between words are recorded in the GAT. We present a hybrid semi-deterministic parser that is controlled by the association values between phrases. Whenever the expectation of the parser fails, it chooses an alternative using a chart to avoid backtracking.