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Although technology has been integrated into vocabulary instruction, to date, few studies have compared whether learning management system (LMS) vocabulary exercises or vocabulary online games facilitate better vocabulary acquisition. The purpose of this study was to investigate whether the Test of English for International Communication (TOEIC) vocabulary lessons plus LMS exercises and TOEIC vocabulary lessons plus MultiEx games (online games) foster short-term vocabulary memorization and long-term vocabulary retention, as well as which performed better. Participants were 72 first-year students at a university in southern Thailand. They were divided into two experimental groups, one given LMS exercises and the other MultiEx games. A word list was taken from the TOEIC word list and a pre-test was used to determine how many words students knew. The unknown words were used in the design of the vocabulary lessons. Ten lessons were provided for the students. Immediately after each lesson, a post-test was conducted to measure their vocabulary recognition. Two weeks after the final lesson, a delayed post-test was conducted to determine how many of the new words had been retained. The main finding was that both vocabulary memorization and retention were enhanced through the use of LMS exercises and the use of MultiEx games. The results showed a higher mean score for the MultiEx game group in both the immediate post-tests and the delayed post-test. Although the differences between the two groups were not statistically significant, the findings suggest integrating technology enhances vocabulary learning outcomes.

Nattapong TONGTEP †a) , Student Member and Thanaruk THEERAMUNKONG †b) , Member SUMMARYExtracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work for, live in, located in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored. key words: relation extraction, named entity, surface feature, information extraction IntroductionRecently several information extraction (IE) approaches have been proposed to transform an unstructured text into knowledge base, such as those in [1 [25] presented a so-called CORDER system to find relations among entities in an organization's documents on a social network. The mined knowledge was in the form of who works with whom, on which projects and with which customers, using strength measured for each co-occurring NE based on its co-occurrences and distances with the target. The CORDER comprised the steps of data selection, named entity recognition and ranking by relation strengths.As an integrated community project, tasks of entity and relation extraction from English, Chinese and Arabic texts were conducted in the Automatic Content Extraction (ACE) program * , including three sets of annotation tasks; Entity Detection and Tracking (EDT), Relation Detection and Characterization (RDC), and Event Detection and Characterization (EDC) [26]. Three main EDT tasks were the detection of entities mentioned in a document, the tracking of entities * http://projects.ldc.upenn.edu/ace/

Nattapong TONGTEP†a) , Student Member and Thanaruk THEERAMUNKONG †b) , Member SUMMARY Automated or semi-automated annotation is a practical solution for large-scale corpus construction. However, the special characteristics of Thai language, such as lack of word-boundary and sentenceboundary markers, trigger several issues in automatic corpus annotation. This paper presents a multi-stage annotation framework, containing two stages of chunking and three stages of tagging. The two chunking stages are pattern matching-based named entity (NE) extraction and dictionarybased word segmentation while the three succeeding tagging stages are dictionary-, pattern-and statist09812490981249ical-based tagging. Applying heuristics of ambiguity priority, NE extraction is performed first on an original text using a set of patterns, in the order of pattern ambiguity. Next, the remaining text is segmented into words with a dictionary. The obtained chunks are then tagged with types of named entities or parts-of-speech (PoS) using dictionaries, patterns and statistics. Focusing on the reduction of human intervention in corpus construction, our experimental results show that the dictionary-based tagging process can assign unique tags to 64.92% of the words, with the remaining of 24.14% unknown words and 10.94% ambiguously tagged words. Later, the pattern-based tagging can reduce unknown words to only 13.34% while the statistical-based tagging can solve the ambiguously tagged words to only 3.01%.