Aspect-based sentiment analysis (ABSA) is a task of identifying fine-grained sentiment entities in a given sentence, which is generally formulated as a sequence labeling problem. Recently, advancements in large pre-tr...
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Conventional spoken term detection (STD) techniques, which use a text-based matching approach based on automatic speech recognition (ASR) systems, are not robust for speech recognition errors. This paper proposes a co...
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ISBN:
(纸本)9786163618238
Conventional spoken term detection (STD) techniques, which use a text-based matching approach based on automatic speech recognition (ASR) systems, are not robust for speech recognition errors. This paper proposes a conditional random fields (CRF)-based re-ranking approach, which recomputes detection scores produced by a phoneme -based dynamic time warping (DTW) STD approach. In the re-ranking approach, we tackle STD as a sequence labeling problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme -based transcriptions. They train recognition error patterns such as phoneme-to-phoneme confusions on the CRF framework. Therefore, the models can detect a triphone, which is one of triphones composing a query term, with detection probability. In the experimental evaluation on the Japanese OOV test collection, the CRF-based approach alone could not outperform the conventional DTW-based approach we have already proposed;however, it worked well in the re-ranking (second-pass) process for the detections from the DTW-based approach. The CRF-based re-ranking approach made a 2.4% improvement of F-measure in the STD performance.
Proteins are complex molecules, each comprised of its own combination of twenty different amino acids. Protein secondary structure is a polypeptide that has formed an arrangement of amino acids that are located next t...
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ISBN:
(纸本)9788132204909
Proteins are complex molecules, each comprised of its own combination of twenty different amino acids. Protein secondary structure is a polypeptide that has formed an arrangement of amino acids that are located next to one another in a linear fashion. Protein secondary structure prediction refers to the prediction of the conformational state of each amino acid residue of a protein sequence as one of the three possible states, namely helices, strands, or coils, denoted as H, E, and C, respectively. Protein sequence is the only resource that provides the information to survive denaturing process, so it is essential to find the secondary structure of a protein sequence. The existing methodology uses only one hydrophobicity scale called Kyte-Doolittle whereas in this paper three scales such as, Kyte-Doolittle scale, Hopp-Woods scale and Rose scale are used for protein secondary structure prediction. This Paper formulates secondary structure prediction task as sequencelabeling and a new coding scheme is introduced with multiple windows to predict secondary structure of proteins using hydrophobicity scales. Protein sequences with their physical and chemical properties are learned using SVMhmm that creates a learned model, which is then used to predict protein secondary structure of an unknown primary sequence. It is reported 77.11% accuracy based on Q(3) measures, when SVMhmm is used.
Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve ...
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ISBN:
(纸本)9780769536415
Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.
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