Canasai Kruengkrai

An Error-Driven Word-Character Hybrid Model for Joint Chinese Word Segmentation and POS Tagging

In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an error-driven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus. We describe an efficient framework for training our model based on the Margin Infused Relaxed Algorithm (MIRA), evaluate our approach on the Penn Chinese Treebank, and show that it achieves superior performance compared to the state-of-the-art approaches reported in the literature.

Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network.

This paper proposes a method for intrasentential subject zero anaphora resolution in Japanese. Our proposed method utilizes a Multi-column Convolutional Neural Network (MCNN) for predicting zero anaphoric relations. Motivated by Centering Theory and other previous works, we exploit as clues both the surface word sequence and the dependency tree of a target sentence in our MCNN. Even though the F-score of our method was lower than that of the state-of-the-art method, which achieved relatively high recall and low precision, our method achieved much higher precision (>0.8) in a wide range of recall levels. We believe such high precision is crucial for real-world NLP applications and thus our method is preferable to the state-of-the-art method.

Construction of Thai Lexicon from Existing Dictionaries and Texts on the Web

A lexicon is an important linguistic resource needed for both shallow and deep language processing. Currently, there are few machine-readable Thai dictionaries available, and most of them do not satisfy the computational requirements. This paper presents the design of a Thai lexicon named the TCLs Computational Lexicon (TCLLEX) and proposes a method to construct a large-scale Thai lexicon by re-using two existing dictionaries and a large number of texts on the Internet. In addition to morphological, syntactic, semantic case role and logical information in the existing dictionaries, a sort of semantic constraint called selectional preference is automatically acquired by analyzing Thai texts on the web and then added into the lexicon. In the acquisition process of the selectional preferences, the so-called Bayesian Information Criterion (BIC) is applied as the measure in a tree cut model. The experiments are done to verify the feasibility and effectiveness of obtained selection preferences.

Document clustering using linear partitioning hyperplanes and reallocation

This paper presents a novel algorithm for document clustering based on a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm [1] and a simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm. The idea of the PDDP algorithm is to recursively split data samples into two sub-clusters using the hyperplane normal to the principal direction derived from the covariance matrix. However, the PDDP algorithm can yield poor results, especially when clusters are not well-separated from one another. To improve the quality of the clustering results, we deal with this problem by re-allocating new cluster membership using the sGEM algorithm with different settings. Furthermore, based on the theoretical background of the sGEM algorithm, we can naturally extend the framework to cover the problem of estimating the number of clusters using the Bayesian Information Criterion. Experimental results on two different corpora are given to show the effectiveness of our algorithm.

An EM-Based Approach for Mining Word Senses from Corpora

Manually collecting contexts of a target word and grouping them based on their meanings yields a set of word senses but the task is quite tedious. Towards automated lexicography, this paper proposes a word-sense discrimination method based on two modern techniques; EM algorithm and principal component analysis (PCA). The spherical Gaussian EM algorithm enhanced with PCA for robust initialization is proposed to cluster word senses of a target word automatically. Three variants of the algorithm, namely PCA, sGEM, and PCA-sGEM, are investigated using a gold standard dataset of two polysemous words. The clustering result is evaluated using the measures of purity and entropy as well as a more recent measure called normalized mutual information (NMI). The experimental result indicates that the proposed algorithms gain promising performance with regard to discriminate word senses and the PCA-sGEM outperforms the other two methods to some extent.

Analysis of an iterative algorithm for term-based ontology alignment

This paper analyzes the results of automatic concept alignment between two ontologies. We use an iterative algorithm to perform concept alignment. The algorithm uses the similarity of shared terms in order to find the most appropriate target concept for a particular source concept. The results show that the proposed algorithm not only finds the relation between the target concepts and the source concepts, but the algorithm also shows some flaws in the ontologies. These results can be used to improve the correctness of the ontologies.

Effectiveness of Keyword and Semantic Relation Extraction for Knowledge Map Generation

We explore the named entity (NE) recognition and semantic relation extraction technique on the Thai cultural database. Within the limited domain and well-structured database, our proposed method can perform in an acceptable high accuracy to generate the tuples of semantic relation for expressing the essence of the record in terms of infobox and knowledge map. In this paper, we propose a semantic relation extraction approach based on simple relation templates that determine relation types and their arguments. We attempt to reduce semantic drift of the arguments by using named entity models as semantic constraints. Experimental results indicate that our approach is very promising. We successfully apply our approach to a cultural database and discover more than 18,000 relation instances with expected high accuracy.

Statistical-Based Approach to Non-segmented Language Processing

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.