Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities
This work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair and concept pair that could be part of causal noun phrase pairs. We also use the cue phrase probability that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. With this probabilistic model, we increase both precision and recall. Our causality extraction shows an F-score of 77.37%, which is an improvement of 21.14 percentage points over the baseline model. The long distance causal relation is extracted with the binary tree-styled cue phrase. We propose an incremental cue phrase learning method based on the cue phrase confidence score that was measured after each causal classifier learning step. A better recall of 15.37 percentage points is acquired after the cue phrase learning.

title: Re-ranking model based on document clusters
abstract: In this paper, we describe a model of information retrieval system that is based on a document re-ranking method using document clusters. In the first step, we retrieve documents based on the inverted-file method. Next, we analyze the retrieved documents using document clusters, and re-rank them. In this step, we use static clusters and dynamic cluster view. Consequently, we can produce clusters that are tailored to characteristics of the query. We focus on the merits of the inverted-file method and cluster analysis. In other words, we retrieve documents based on the inverted-file method and analyze all terms in document based on the cluster analysis. By these two steps, we can get the retrieved results which are made by the consideration of the context of all terms in a document as well as query terms. We will show that our method achieves significant improvements over the method based on similarity search ranking alone.

title: A Korean-Japanese-Chinese Aligned Wordnet with Shared Semantic Hierarchy
abstract: A Korean-Japanese-Chinese aligned wordnet, “CoreNet” is introduced. For the purpose of this paper, the term “wordnet” refers to a network of words. It is constructed based on a shared semantic hierarchy that is originated from NTT Goidaikei (Lexical Hierarchical System). Korean wordnet was constructed through the semantic category assignment to every meaning of Korean words in a dictionary. Verbs and adjectives’ word senses are assigned to the same semantic hierarchy as that of nouns. Each sense of verbs is investigated from corpora for their usage, and compared with Japanese translation. Chinese wordnet with the same semantic hierarchy was built up based on the comparison with Korean wordnet. Each sense of Chinese verb corresponds to Korean with its argument structure. The use of the same semantic hierarchy for nouns and predicates has several advantages. First, the surface forms of nouns and predicates share the similar one, especially in Chinese words. In case of Korean and Japanese, the typical formation is like “do+Noun” in English like “Noun+suru” in Japanese and “Noun+hada” in Korean. Second, the language generation from conceptual structures takes freedom to choose the surface form whether it chooses noun phrases or verb phrases. CoreNet has been constructed by the following principles: word sense mapping to concept, corpus-based, multi-lingualism, and single concept system for multi-languages. The overall flow of construction is based on dictionary-based bootstrapping, incremental similarity-based classification and manual post-editing. Among consideration points, the followings are introduced: multiple concept mapping, verbal noun, and concept splitting. For multiple concept mapping, a word is mapped into numerous concepts that comprise respective meanings of the word. For example, school is an “institution for the instruction of students.” The word school is mapped into three concepts such as location, organization, and facility. For verbal noun, a word that is a verb is assigned to concepts after it is transformed to a noun. For example, “write” is transformed to its noun form “writing” that is mapped into a concept writing falling under event. For concept splitting, every time inconsistency among nodes of concepts is discovered, a node may be added. What differs between CoreNet and NTT Goidaikei is that CoreNet features mapping between word senses (not just words) and concepts. These works have lasted since 1994.

title: Effective foreign word extraction for Korean information retrieval
abstract: In Korean text, foreign words, which are mostly transliterations of English words, are frequently used. Foreign words are usually very important index terms in Korean information retrieval since most of them are technical terms or names. So accurate foreign word extraction is crucial for high performance of information retrieval. However, accurate foreign word extraction is not easy because it inevitably accompanies word segmentation and most of the foreign words are unknown. In this paper, we present an effective foreign word recognition and extraction method. In order to accurately extract foreign words, we developed an effective method of word segmentation that involves unknown foreign words. Our word segmentation method effectively utilizes both unknown word information acquired through the automatic dictionary compilation and foreign word recognition information. Our HMM-based foreign word recognition method does not require large labeled examples for the model training unlike the previously proposed method.

title: Implicit ambiguity resolution using incremental clustering in cross-language information retrieval
abstract: This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in cross-language information retrieval (CLIR) such as Korean-to-English and Japanese-to-English CLIR. The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is recalculated by using the clusters. In the experiment based on TREC CLIR test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvement for all translation queries, compared with blind feedback for the probabilistic retrieval in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity.

title: A Comparison of Different Machine Transliteration Models
abstract: Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.

title: Research problems in clothing simulation
abstract: Clothing simulation and animation are of great importance in computer animation. If cloth simulations could be improved to the point that they could generate realistic cloth motion in real-time, they would find uses in many aspects of daily life such as in fashion design and manufacturing. The area of cloth simulation and animation is full of technical challenges: creating more realistic results, achieving faster run-times, and developing methods capable of constructing and simulating more complex garments. This paper provides an overview of the key procedures involved in the creation of clothed characters, describes the current state-of-the-art techniques, and proposes the research problems that most require further study. Three technical aspects of cloth simulation are considered in this paper: garment construction, physically based simulation, and collision resolution.

title: Multiple Genome Alignment by Clustering Pairwise Matches
abstract: We have developed a multiple genome alignment algorithm by using a sequence clustering algorithm to combine local pairwisegenome sequence matches produced by pairwise genome alignments, e.g, BLASTZ. Sequence clustering algorithms often generate clusters of sequences such that there exists a common shared region among all sequences in each cluster. To use a sequence clustering algorithm for genome alignment, it is necessary to handle numerous local alignments between a pair of genomes. We propose a multiple genome alignment method that converts the multiple genome alignment problem to the sequence clustering problem. This method does not need to make a guide tree to determine the order of multiple alignment, and it accurately detects multiple homologous regions. As a result, our multiple genome alignment algorithm performs competitively over existing algorithms. This is shown using an experiment which compares the performance of TBA, MultiPipMaker (MPM) and our algorithm in aligning 12 groups of 56 microbial genomes and by evaluating the number of common COGs detected.

title: Off-chip latency-driven dynamic voltage and frequency scaling for an MPEG decoding
abstract: This paper describes a dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption using the computational workload decomposition. This technique decomposes the workload for decoding a frame into on-chip and off-chip workloads. The execution time required for the on-chip workload is CPU frequency-dependent, whereas the off-chip workload execution time does not change, regardless of the CPU frequency, resulting in the maximum energy savings by setting the minimum frequency during off-chip workload execution time, without causing any delay penalty. This workload decomposition is performed using a performance-monitoring unit (PMU) in the XScale-processor, which provides various statistics such as cache hit/miss and CPU stall, due to data dependency at run time. The on-chip workload for an incoming frame is predicted using a frame-based history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode the frame. To guarantee a quality of service (QoS) constraint, a prediction error compensation method, called inter-frame compensation, is proposed in which the on-chip workload prediction error is diffused into subsequent frames such that run time frame rates change smoothly. The proposed DVFS algorithm has been implemented on an XScale-based Testbed. Detailed current measurements on this platform demonstrate significant CPU energy savings ranging from 50% to 80% depending on the video clip.

title: Sensitivity analysis and efficient method for identifying optimal spaced seeds
abstract: The novel introduction of spaced seed idea in the filtration stage of sequence comparison by Ma et al. (Bioinformatics 18 (2002) 440) has greatly increased the sensitivity of homology search without compromising the speed of search. Finding the optimal spaced seeds is of great importance both theoretically and in designing better search tool for sequence comparison. In this paper, we study the computational aspects of calculating the hitting probability of spaced seeds; and based on these results, we propose an efficient algorithm for identifying optimal spaced seeds.

title: Palindromes in SARS and Other Coronaviruses
abstract: With the identification of a novel coronavirus associated with the severe acute respiratory syndrome (SARS), computational analysis of its RNA genome sequence is expected to give useful clues to help elucidate the origin, evolution, and pathogenicity of the virus. In this paper, we study the collective counts of palindromes in the SARS genome along with all the completely sequenced coronaviruses. Based on a Markov-chain model for the genome sequence, the mean and standard deviation for the number of palindromes at or above a given length are derived. These theoretical results are complemented by extensive simulations to provide empirical estimates. Using a z score obtained from these mathematical and empirical means and standard deviations, we have observed that palindromes of length four are significantly underrepresented in all the coronaviruses in our data set. In contrast, length-six palindromes are significantly underrepresented only in the SARS coronavirus. Two other features are unique to the SARS sequence. First, there is a length-22 palindrome TCTTTAACAAGCTTGTTAAAGA spanning positions 25962-25983. Second, there are two repeating length-12 palindromes TTATAATTATAA spanning positions 22712-22723 and 22796-22807. Some further investigations into possible biological implications of these palindrome features are proposed.