Lalittrojwong, Pattarachai

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An Ontology-based Multi-agent System for Matchmaking
(2002-12-01)
Pothipruk, Pakornpong
;
Lalitrojwong, Pattarachai
In a multi-agent system (MAS), at least one agent matchmaking service is required to let any agent in the system find some agents that can help it complete some part of its job. This service is normally in the form of a typical agent called matchmaking agent. In this work, Distributed Matchmaking Agent Architecture (DMAA) is proposed. It is a group of matchmaking agents that provide agent matchmaking service and exploit domain ontology to enhance the matchmaking process. It is composed of a group of three-type matchmaking agents working together: root matchmaking agents (RMMAs), authoritative matchmaking agents (AMMAs), and local matchmaking agents (LMMAs). They represent agents' preferences and capabilities by predicate calculus sentences, and use taxonomic knowledge base to semantically match the preferences and the capabilities. The root matchmaking agent also uses the taxonomic knowledge base to perform ontological load balancing between matchmaking agents in the system so that the system workload from its matching process is reduced. This makes the system perform its task faster.
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The use of computer technology by visually impaired high school students in integrated classrooms in Bangkok
(2004-12-01)
Lalitrojwong, Pattarachai
;
Chaisingharn, Namchoke
This research analyzes the use of computer technology by students with visual impairments in the integrated classrooms in Bangkok. The purposes of the research are to acquire the real facts about applying computer technology in the educational programs by visually impaired students, and to identify problems and needs of the visually impaired students in the integrated classrooms. Data is collected through questionnaires and interviews from all the 56 students of population. The acquired data is then analyzed using the SPSS program in statistic terms of frequency, mean, and percentage. The findings obviously demonstrate that the majority of students employ solely screen reading programs and speech synthesizers. In addition, instructors teaching computer technology to the visually impaired students should be effectively trained to be more skillful and to understand limitations of the way those students learn and follow the instructions in a computer laboratory class. The expensiveness of the technology is also one of the problems that ought to be taken into consideration. Moreover, even though the lack of opportunities to apply computer technology in classrooms is mainly concerned, most of the visually impaired students still need to cooperatively enhance their chance through the universal design of computer technology with general students.
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CIM — the hybrid symbolic/connectionist rule-based inference system
(2000-01-01)
Lalitrojwong, Pattarachai
Previous research has shown that connectionist models are suitable for cognitive and natural language processing tasks. An inference mechanism is a key element in commonsense reasoning in a natural language understanding system. This research project offers a connectionist alternative to Buchheit’s symbolic inference module for INFANT called the Connectionist Inference Mechanism (CIM). CIM is a hybrid cognitive model that combines the advantages of the symbolic approach, local representation, and parallel distributed processing. Moreover, it makes good use of its modular structure. Several modules work together in CIM, including memory, neural networks, and a binding set, to perform the inference generation. Besides rule application capability, CIM is also able to perform variable binding. A number of experiments have shown that CIM can make inferences appropriately.
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A maximum entropy model for product feature extraction in online customer reviews
(2008-01-01)
Somprasertsri, Gamgarn
;
Lalitrojwong, Pattarachai
Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. In this paper, we propose an approach to product feature extraction using a maximum entropy model. Maximum entropy is a probability distribution estimation technique. It is widely used for classification problems in natural language processing, such as question answering, information extraction, and part-of-speech tagging. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Using a maximum entropy approach, at first we extract features from the corpus, train maximum entropy model with an annotated corpus, and then use it with additional product feature discovery to extract product features from customer reviews. Our experimental results show that this approach can work effectively for product feature extraction with 71.88% precision and 75.23% recall. © 2008 IEEE.
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Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features
(2008-09-23)
Somprasertsri, Gamgarn
;
Lalitrojwong, Pattarachai
The task of product feature extraction is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about the products. We propose an approach for product feature extraction by combining lexical and syntactic features with a maximum entropy model. For the underlying principle of maximum entropy, it prefers the uniform distributions if there is no external knowledge. Using a maximum entropy approach, firstly we extract the learning features from the annotated corpus, secondly we train the maximum entropy model, thirdly we use trained model to extract product features, and finally we apply a natural language processing technique in postprocessing step to discover the remaining product features. Our experimental results show that this approach is suitable for automatic product feature extraction. ©2008 IEEE.

Lalittrojwong, Pattarachai

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