An important issue in applying machine learning algorithms to Natural Language Processing areas such as Named Entity Recognition tasks is to overcome the lack of tagged corpora. Several bootstrapping methods such as co-training have been proposed as a solution. In this paper, we present a different approach using the Web resources. A Named Entity (NE) tagged corpus is generated from the Web using about 3,000 names as seeds. The generated corpus may have a lower quality than the manually tagged corpus but its size can be increased sufficiently. Several features are developed and the decision list is learned using the generated corpus. Our method is verified by comparing it to both the decision list learned on the manual corpus and the DL-CoTrain method. We also present a two-level classification by cascading highly precise lexical patterns and the decision list to improve the performance.

This paper describes a practical Japanese natural language Question Answering system adopting effective selection of dynamic passages, Lexico-Semantic Patterns (LSP), and Predictive Answer Indexing. By analyzing the previous TREC QA data, we defined a dynamic passage unit and developed a passage selection method suitable for Question Answering. Using LSP, we identify the answer type of a question and detect answer candidates without any deep linguistic analyses of the texts. To guarantee a short response time, Predictive Answer Indexing is combined into our overall system architecture. As a result of the three engineering techniques, our system showed excellent performance when evaluated by mean reciprocal rank (MRR) in NTCIR-3 QAC-1.

Although there have been enormous investments into English education all around the world, not many differences have been made to change the English instruction style. Considering the shortcomings for the current teaching-learning methodology, we have been investigating advanced computer-assisted language learning (CALL) systems. This paper aims at summarizing a set of POSTECH approaches including theories, technologies, systems, and field studies and providing relevant pointers. On top of the state-of-the-art technologies of spoken dialog system, a variety of adaptations have been applied to overcome some problems caused by numerous errors and variations naturally produced by non-native speakers. Furthermore, a number of methods have been developed for generating educational feedback that help learners develop to be proficient. Integrating these efforts resulted in intelligent educational robots – Mero and Engkey – and virtual 3D language learning games, Pomy. To verify the effects of our approaches on students' communicative abilities, we have conducted a field study at an elementary school in Korea. The results showed that our CALL approaches can be enjoyable and fruitful activities for students. Although the results of this study bring us a step closer to understanding computer-based education, more studies are needed to consolidate the findings.

This study examines the dialog-based language learning game (DB-LLG) realized in a 3D environment built with game contents. We designed the DB-LLG to communicate with users who can conduct interactive conversations with game characters in various immersive environments. From the pilot test, we found that several technologies were identified as essential in the construction of the DB-LLG such as dialog management, hint generation, and grammar error detection and feedback. We describe the technical details of our system POSTECH immersive English study (Pomy). We evaluated the performance of each technology using a simulator and field tests with users.

This paper describes a highly-portable multilingual question answering system on multiple relational databases. We apply techniques which were verified on open-domain text-based question answering, such as semantic category and pattern-based grammars, into natural language interfaces to relational databases. Lexico-semantic pattern (LSP) and multi-level grammars achieve portability of languages, domains, and DB management systems. The LSP-based linguistic processing does not require deep analysis that sacrifices robustness and flexibility, but can handle delicate natural language questions. To maximize portability, we drive three dependency factors into the following two parts: language-dependent part into front linguistic analysis, and domain-dependent and database-dependent parts into backend SQL query generation. We also support session-based dialog by preserving SQL queries created from previous user's question, and then re-generating new SQL query for the successive questions. Experiments with 779 queries generate only constraint-missing errors, which can be easily corrected by adding new terms, of 2.25% for English and 5.67% for Korean.

We propose a one-step error detection and correction interface for a voice word processor. This correction interface performs analysis region detection, user intention understanding and error correction utterance recognition, all from a single user utterance input. We evaluate the performance of each component first, and then compare the effectiveness of our interface to two previous interfaces. Our evaluation demonstrates that each component is technically superior to the baselines and that our one-step error detection and correction method yields an error correction interface that is more convenient and natural than the two previous interfaces.

Spoken language understanding (SLU) aims to map a user's speech into a semantic frame. Since most of the previous works use the semantic structures for SLU, we verify that the structure is useful even for noisy input. We apply a structured prediction method to SLU problem and compare it to an unstructured one. In addition, we present a combined method to embed long-distance dependency between entities in a cascaded manner. On air travel data, we show that our approach improves performance over baseline models.