GuideLink: A Corpus Annotation System that Integrates the Management of Annotation Guidelines

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Design of Chinese HPSG Framework for Data-Driven Parsing

PACLIC 23 / City University of Hong Kong / 3-5 December 200

EFFICACY OF INTERFERON THERAPY FOR CHRONIC HEPATITIS C ： A COOPERATIVE STUDY IN ELEVEN HOSPITALS

We investigated the influences of liver histology，serum levels of hepatitis C virus (HCV) and HCV genotypes on responsiveness to interferon (IFN) therapy in 342 patients with chronic hepatitis C. Either 9 million units (MU) of lymphoblastoid alpha IFN or 3 MU of recombinant IFN-alpha was administered daily for 2 weeks and then three times a week for 22 weeks. IFN responses were divided into three groups on the basis of the results of polymerase chain reaction (PCR) assay detecting HCV-RNA in serum. Complete response (CR) was defined as sustained elimination of HCV for at least 6 months after treatment，partial response (PR) as HCV elimination for a limited period，non-response (NR) as continuously positive for HCV-RNA in serum. Quantitation of pre-treament HCV-RNA amount in serum was determined by competitive PCR assay in 47 patients. HCV genotyping was performed in 114 patients by PCR with genotype-specific primers. CR was obtained in 97 patients (28.4%)，PR in 104 (30.4%) and NR in 141 (41.2%). IFN responses，represented by CR/PR/NR，were 15/18/11 in 44 patients with chronic persistent hepatitis (CPH)，72/65/73 in 210 patients with chronic aggressive hepatitis (CAH) 2a，and 10/21/57 in 88 patients with CAH2b. CR rate was lower in patients with CAH2b (11.4%) compared to those with CPH (34.1%) or CAH2a (34.3%). Averages of pre-treatment serum HCV-RNA amount (copies/50μl) were 10³·⁵⁵ in 13 CRs，10⁴·⁵⁶ in 17 PRs，and 10⁵·⁹⁵ in 17 NRs. There was a positive correlation between pre-treatment HCV-RNA levels and IFN unresponsiveness. HCV genotyping in 114 patients revealed that HCV type Ⅰ infection was observed in one，type Ⅱ in 94，type Ⅲ in 11，type Ⅳ in 6 and mixed (types Ⅱ and Ⅳ) in 2 patients，and their IFN responses (CR/PR/NR) were 0/0/1，28/26/40，3/5/3，1/3/2 and 0/1/1，respectively

Named Entity Recognition for Bacterial Type IV Secretion Systems

Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents

Planar-type silicon thermoelectric generator with phononic nanostructures for 100 {\mu}W energy harvesting

Energy harvesting is essential for the internet-of-things networks where a tremendous number of sensors require power. Thermoelectric generators (TEGs), especially those based on silicon (Si), are a promising source of clean and sustainable energy for these sensors. However, the reported performance of planar-type Si TEGs never exceeded power factors of 0.1 ${\mu} Wcm^{-2} K^{-2}$ due to the poor thermoelectric performance of Si and the suboptimal design of the devices. Here, we report a planar-type Si TEG with a power factor of 1.3 ${\mu} Wcm^{-2} K^{-2}$ around room temperature. The increase in thermoelectric performance of Si by nanostructuring based on the phonon-glass electron-crystal concept and optimized three-dimensional heat-guiding structures resulted in a significant power factor. In-field testing demonstrated that our Si TEG functions as a 100-${\mu}W$-class harvester. This result is an essential step toward energy harvesting with a low-environmental load and cost-effective material with high throughput, a necessary condition for energy-autonomous sensor nodes for the trillion sensors universe

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR