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SENER: Sentiment Element Named Entity Recognition for Aspect-Based Sentiment Analysis 48

SENER: Sentiment Element Named Entity Recognition for Aspect...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lee, Sun-Kyung Kim, Jong-Hwan Kaist Daejeon Korea Republic of

ISBN: (纸本)9781728163277

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-trained language models (PLMs) led to generative ABSA, where the task is treated as text-to-text transition resolved by fine-tuning PLMs. Although the generative methods are designed to capture sentence-level semantic information, they are inappropriate for explicit comprehension of sentiment structure. In order to address this issue, we propose sentiment element named entity recognition (SENER) for ABSA. SENER integrates the concepts of named entity recognition (NER) and generative ABSA to retrieve the sentiment entities with predefined sentiment element names, leading to better semantic and sentiment structure understanding. Experimental results on several ABSA tasks show that the proposed SENER significantly outperforms previous works on ASQP and ASTE. © 2023 IEEE.

关键词： Aspect-based Sentiment Analysis Named Entity Recognition Pre-trained Language Models sequence labeling problem

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Re-Ranking of Spoken Term Detections Using CRF-based Triphone Detection Models

Re-Ranking of Spoken Term Detections Using CRF-based Triphon...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA)

作者： Sawada, Naoki Natori, Satoshi Nishizaki, Hiromitsu Univ Yamanashi Interdisciplinary Grad Sch Med & Engn Dept Educ Kofu Yamanashi 4008511 Japan

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.

关键词： pattern matching random processes speech recognition text analysis CRF-based re-ranking approach CRF-based triphone detection model DTW-based approach F-measure Japanese OOV test collection conditional random field detection score recomputation phoneme-based dynamic time warping phoneme-based transcriptions recognition error patterns sequence labeling problem spoken term detection text-based matching approach Feature extraction Hidden Markov models Indexes Probability Speech Speech recognition Training

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Label sequence Learning Based Protein Secondary Structure Prediction Using Hydrophobicity Scales

Label Sequence Learning Based Protein Secondary Structure Pr...

引用

International Conference on Soft Computing for problem Solving (SocProS 2011)

作者： Vinodhini, R. Vijaya, M. S. PSGR Krishnammal Coll Women Coimbatore 641004 Tamil Nadu India GRG Sch Appl Comp Technol Coimbatore 641004 Tamil Nadu India

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 sequence labeling 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.

关键词： Protein Protein Secondary Structure sequence labeling problem Hydrophobicity Scales.

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Protein Secondary Structure Prediction using Large Margin Methods

Protein Secondary Structure Prediction using Large Margin Me...

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8th IEEE/ACIS International Conference on Computer and Information Science

作者： Tang, Buzhou Wang, Xuan Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

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.

关键词： Protein secondary structure prediction large margin approach sequence labeling problem probabilistic graphical models

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