Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. Categorized information can help us quickly collect emerging problems in order to provide a support needed. Indeed, the response to the complaint without the delay will grant users highest satisfaction. In this paper, we aim to deliver a novel approach which can clarify the complaints precisely with the aim to classify each complaint into nine predefined classes i.e. acces-sibility, company brand, competitors, facilities, process, product feature, staff quality, timing respec-tively and others. Given the idea that one word usually conveys ambiguity and it has to be interpreted by its context, the word embedding technique is used to provide word features while applying deep learning techniques for classifying a type of complaints. The dataset we use contains 8,439 complaints of one company.

This paper presents the language resource management system for the development and dissemination of Asian WordNet (AWN) and its web service application. We develop the platform to establish a network for the cross language WordNet development. Each node of the network is designed for maintaining the WordNet for a language. Via the table that maps between each language WordNet and the Princeton WordNet (PWN), the Asian WordNet is realized to visualize the cross language WordNet between the Asian languages. We propose a language resource management system, called WordNet Management System (WNMS), as a distributed management system that allows the server to perform the cross language WordNet retrieval, including the fundamental web service applications for editing, visualizing and language processing. The WNMS is implemented on a web service protocol therefore each node can be independently maintained, and the service of each language WordNet can be called directly through the web service API. In case of cross language implementation, the synset ID (or synset offset) defined by PWN is used to determined the linkage between the languages.

Corpus-based approaches and statistical approaches have been the main stream of natural language processing research for the past two decades. Language resources play a key role in such approaches, but there is an insufficient amount of language resources in many Asian languages. In this situation, standardisation of language resources would be of great help in developing resources in new languages. This paper presents the latest development efforts of our project which aims at creating a common standard for Asian language resources that is compatible with an international standard. In particular, the paper focuses on i) lexical specification and data categories relevant for building multilingual lexical resources for Asian languages; ii) a core upper-layer ontology needed for ensuring multilingual interoperability and iii) the evaluation platform used to test the entire architectural framework.

This paper presents some preliminary results of our dependency parser for Thai. It is part of an ongoing project in developing a syntactically annotated Thai corpus. The parser has been trained and tested by using the complete part of the corpus. The parser achieves 83.64% as the root accuracy, 78.54% as the dependency accuracy and 53.90% as the complete sentence accuracy. The trained parser will be used as a preprocessing step in our corpus annotation workflow in order to accelerate the corpus development.

This paper compares the effectiveness of two different Thai search engines by using a blind evaluation. The probabilistic-based dictionary-less search engine is evaluated against the traditional word-based indexing method. The web documents from 12 Thai newspaper web sites consisting of 83,453 documents are used as the test collection. The relevance judgment is conducted on the first five returned results from each system. The evaluation process is completely blind. That is, the retrieved documents from both systems are shown to the judges without any information about thesearch techniques. Statistical testing shows that the dictionary-less approach is better than the word-based indexingapproach in terms of the number of found documents and the number of relevance documents.

This paper presents a framework for Thai morphological analysis based on the theoretical background of conditional random fields. We formulate morphological analysis of an unsegmented language as the sequential supervised learning problem. Given a sequence of characters, all possibilities of word/tag segmentation are generated, and then the optimal path is selected with some criterion. We examine two different techniques, including the Viterbi score and the confidence estimation. Preliminary results are given to show the feasibility of our proposed framework.

The growing of multilingual information processing technology has created the need of linguistic resources, especially lexical database. Many attempts were put to alter the traditional dictionary to computational dictionary, or widely named as computational lexicon. TCL’s Computational Lexicon (TCLLEX) is a recent development of a large-scale Thai Lexicon, which aims to serve as a fundamental linguistic resource for natural language processing research. We design either terminology or ontology for structuring the lexicon based on the idea of computability and reusability.

This paper presents a new formalization of probabilistic GLR language modeling for statistical parsing. Our model inherits its essential features from Briscoe and Carroll’s generalized probabilistic LR model, which obtains context-sensitivity by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll’s model, however, has a drawback in that it is not formalized in any probabilistically well-founded way, which may degrade its parsing performance. Our formulation overcomes this drawback with a few significant refinements, while maintaining all the advantages of Briscoe and Carroll’s modeling.