An ensemble of transliteration models for information retrieval
Transliteration is used to phonetically translate proper names and technical terms especially from languages in Roman alphabets to languages in non-Roman alphabets such as from English to Korean, Japanese, and Chinese. Because transliterations are usually representative index terms for documents, proper handling of the transliterations is important for an effective information retrieval system. However, there are limitations on handling transliterations depending on dictionary lookup, because transliterations are usually not registered in the dictionary. For this reason, many researchers have been trying to overcome the problem using machine transliteration. In this paper, we propose a method for improving machine transliteration using an ensemble of three different transliteration models. Because one transliteration model alone has limitation on reflecting all possible transliteration behaviors, several transliteration models should be complementary used in order to achieve a high-performance machine transliteration system. This paper describes a method about transliteration production using the several machine transliteration models and transliteration ranking with web data and relevance scores given by each transliteration model. We report evaluation results for our ensemble transliteration model and experimental results for its impact on IR effectiveness. Machine transliteration tests on English-to-Korean transliteration and English-to-Japanese transliteration show that our proposed method achieves 78-80% word accuracy. Information retrieval tests on KTSET and NTCIR-1 test collection show that our transliteration model can improve the performance of an information retrieval system about 10-34%.

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: 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: Determining the specificity of terms using inside-outside information: a necessary condition of term hierarchy mining
abstract: This paper introduces new specificity measuring methods of terms using inside and outside information. Specificity of a term is the quantity of domain specific information contained in the term. Specific terms have a larger quantity of domain information than general terms. Specificity is an important necessary condition for building hierarchical relations among terms. If t1 is a hyponym of t2 in a domain term hierarchy, then the specificity of t1 is greater than that of t2. As domain specific terms are commonly compounds of the generic level term and some modifiers, inside information is important to represent the meaning of terms. Outside contextual information is also used to complement the shortcomings of inside information. We propose an information theoretic method to measure the quantity of terms. Experiments showed promising results with a precision of 73.9% when applied to terms in the MeSH thesaurus.

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: Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities
abstract: 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: Energy-Aware Wireless Video Streaming
abstract: This paper presents a dynamic energy management policy for a wireless video streaming system, consisting of battery-powered client and server. The paper starts from the observation that the video quality in wireless streaming is a function of three factors: encoding aptitude of the server, decoding aptitude of the client, and the wireless channel. Based on this observation, the energy consumption of a wireless video streaming system is modeled and analyzed. Using the proposed model, the optimal energy assignment to each video frame is done such that the maximum system lifetime is achieved while satisfying a given minimum video quality requirement. Experimental results show that the proposed policy increases the system lifetime by 20%.

title: Dynamic voltage and frequency scaling based on workload decomposition
abstract: This paper presents a technique called "workload decomposition" in which the CPU workload is decomposed in two parts: on-chip and off-chip. The on-chip workload signifies the CPU clock cycles that are required to execute instructions in the CPU whereas the off-chip workload captures the number of external memory access clock cycles that are required to perform external memory transactions. When combined with a dynamic voltage and frequency scaling (DVFS) technique to minimize the energy consumption, this workload decomposition method results in higher energy savings. The workload decomposition itself is performed at run time based on statistics reported by a performance monitoring unit (PMU) without a need for application profiling or compiler support. We have implemented the proposed DVFS with workload decomposition technique on the BitsyX platform, an Intel PXA255-based platform manufactured by ADS Inc., and performed detailed energy measurements. These measurements show that, for a number of widely used software applications, a CPU energy saving of 80% can be achieved for memory-bound programs while satisfying the user-specified timing constraints.

title: System level power-performance trade-offs in embedded systems using voltage and frequency scaling of off-chip buses and memory
abstract: In embedded systems, off-chip buses and memory (i.e., L2 memory as opposed to the L1 memory which is usually on-chip cache) consume significant power often more than the processor itself. In this paper for the case of an embedded system with one processor chip and one memory chip, we propose frequency and voltage scaling of the off-chip buses and the memory chip and use a known micro-architectural enhancement called a store buffer to reduce the resulting impact on execution time. Our benchmarks show a system (processor + off-chip bus + off-chip memory) power savings of 28% to 36%, an energy savings of 13% to 35%, all while increasing the execution time in the range of 1% to 29%. Previous work in power-aware computing has focused on frequency and voltage scaling of the processors or selective power-down of sub-sets of off-chip memory chips. This paper quantitatively explores voltage/frequency scaling of off-chip buses and memory as a means of trading off performance for power/energy at the system level in embedded systems.

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: PLATCOM: Current Status and Plan for the Next Stages
abstract: We have been developing a web-based system for comparing multiple genomes, PLATCOM, where users can choose genomes of their choice freely and perform analysis of the selected genomes with a suite of computational tools. PLATCOM is built on internal databases such as GenBank, COG, KEGG, and Pairwise Comparison Database (PCDB) that contains all pairwise comparisons (97,034 entries) of protein sequence files (.faa) and whole genome sequence files (.fna) of 312 replicons. Since combining multiple tools for sequence analysis requires a significant amount of programming work and knowledge on each tool, we also developed and incorporated high performance sequence data mining tools such as sequence clustering and neighborhood prediction. The next plan includes defining several data types for genome analysis and integrating system modules using data mining tools that operate on the genome data types. PLATCOM is available at http://platcom.informatics.indiana.edu.