107,924 research outputs found
Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis
Get PDFChun Tu and Min-Yuh Day (2013), "Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis", 2013 IEEE International Workshop on Empirical Methods for Recognizing Inference in Text (IEEE EM-RITE 2013), August 14, 2013, in Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), San Francisco, California, USA, August 14-16, 2013, pp. 69-74.Recognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which addressing major text understanding in information access research areas. In this paper, we proposed a Chinese textual entailment system using Wordnet semantic and dependency syntactic approaches in Recognizing Inference in Text (RITE) using the NTCIR-10 RITE-2 subtask datasets. Wordnet is used to recognize entailment at lexical level. Dependency syntactic approach is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis. We thoroughly evaluate our approach using NTCIR-10 RITE-2 subtask datasets. As a result, our system achieved 73.28% on Traditional Chinese Binary-Class (BC) subtask and 74.57% on Simplified Chinese Binary-Class subtask with NTCIR-10 RITE-2 development datasets. Thorough experiments with the text fragments provided by the NTCIR-10 RITE-2 subtask showed that the proposed approach can improve system's overall accuracy.IEEEEI國際20130814~20130816電子版YSan Francisco, US
Appendix 8: Quantifying the effects of environmental factors on catch rates in South-East fisheries: 1986-2006
No full textThe collection of basic environmental data by industry members was successful and offers a way of overcoming the problems associated with differences in scale between the environment and fisheries datasets. A simple method of collecting environmental data was developed that was only a small time burden on skippers, yet has the potential to provide very useful information on the same scale as the catch and effort data recorded in the logbooks. The success of this trial was aided by the natural interest of fishers to learn more about the environment in which they fish. The archival temperature-depth tags chosen proved robust, reliable and easy to use. While the use of large scale environmental data may not yield significant improvements in stock assessments for most SESSF species, fine-scale data collected from selected vessels using methods developed during this project may, in the longer term, be useful for incorporation into CPUE standardisations in the future..
A Study of Evaluation Model of User Satisfaction with Social Network Services
No full text資訊管理研究領域，已由過去探討功能性導向資訊系統，開始逐漸重視娛樂性導向資訊系統的滿意度與使用行為。目前，具有娛樂性導向資訊系統特性之社交網路服務(SNS)也開始受到相當程度的重視。本研究整合期望確認理論、理性行為理論與動機理論建構出一個社交網路服務使用者滿意度評量模式，這個模式包括了確認、主觀規範、認知有用性、認知樂趣、滿意度與持續使用意向等六個構念，實證研究結果顯示，本研究所提出的模式具有良好的可行性與適宜性。研究結果發現，確認、主觀規範、認知有用性、與認知樂趣，為使用者滿意度與持續使用意向的重要因素。本研究主要貢獻為在資訊管理研究領域，較少有研究探討主觀規範與滿意度的關係，本研究填補了資訊管理研究領域文獻對於主觀規範與滿意度之間關係理論的探討與驗證。Research on user satisfaction and behavior has resulted in increased attention being given to hedonic information system in the information systems (IS) literature. Social network services (SNS) with the characteristics of hedonic information systems are attracting increasing attention in the IS domain In this paper, we proposed an evaluation model of user satisfaction with social network services based on the integration of expectation confirmation theory (ECT), the theory of reason action (TRA), and motivation theory. The proposed research model is comprised of six constructs: confirmation, subjective norm, perceived usefulness, perceived enjoyment, satisfaction, and continuance intention. Empirical results show that the proposed model has a good fit in theoretical and practical application. The results revealed that confirmation, perceived enjoyment, and subjective norm are the determinants of user satisfaction and continuance intention. The main research contribution of the study is that little research has been done on understanding the relationship of subjective norm and user satisfaction, this study fill the research gap in the IS literature by exploring and verifying the theoretical relations of subjective norm
A Comparative Study of Data Mining Techniques for Credit Scoring in Banking
Get PDFShih-Chen Huang and Min-Yuh Day (2013), "A Comparative Study of Data Mining Techniques for Credit Scoring in Banking", in Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), San Francisco, California, USA, August 14-16, 2013, pp. 684-691.Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.IEEEEI國際20130814~20130816電子版YSan Francisco, California, US
Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns
No full textThe sentiments of market participants may be aroused when a sharp rise (fall) in oil prices is emitted. In this study, we take the trading signal emitted by the technical indicator into account accompanied with the sharp rise (fall) in oil prices into account in trading stocks. We explore whether investors will profit by trading stocks when a sharp rise (fall) in oil prices and technical trading signal are emitted together. Owing to big data concerns in employing the constituent stocks of DJ 30, FTSE 100, and SSE 50 as our samples, investors can beat the market in trading stocks. The sharp fall in oil prices, such as over 5%, and the oversold technical trading signals by the SOI occurring together can lead to better performance than trading stocks and the sharp movement in oil prices emitted only. Results revealed are for trading the constituent stocks of DJ 30, FTSE 100, and SSE 50 without exception after taking big data into account.補正完
Do sharp movements in oil prices matter for stock markets?
No full textSharp movements, including sharp rise and fall of oil prices, may cause stock market fluctuations due to investors’ sentiments aroused. This study pioneers the exploration of trading performance when a sharp rise (fall) in oil prices occurs. We reveal several findings by employing the constituent stocks of DJ 30, FTSE 100, and SSE 50 as our samples. First, investors may profit from trading stocks after over 10% rise in oil prices because such an increase may be regarded as a positive signal of a momentum phenomenon. Second, continuous 2.5% and 5% fall in oil prices for two or even three days can be regarded as positive signals for China because the country is regarded as the largest oil-importing country. Third, trading these constituents’ stocks after over 10% fall in oil prices may result in a stock price rebound.補正完
Gold standard of UK degrees is lost in translation
No full textInflated marks, overworked staff and politically compromised courses are the price of exploiting offshore UK registered students, says Michael Day
日內大幅價格變化對交易指數期貨重要嗎？中國期貨市場的證據
Get PDFBy employing intraday tick data due to big data concerns, we examine whether investors profit by day trading China Stock Index 300 Futures (C300F) as the C300F index rises (falls) over considerable points in a minute defined as intraday large price change. We argue that the intraday large price change might induce investors to trade the C300F. Results reveal that investors are likely to make profits by taking short positions on the C300F right after the occurrence of the intraday large price change, except when the C300F falls from extremely high points like 20 points in a minute.補正完
IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-10 RITE-2
Get PDFMin-Yuh Day, Chun Tu, Shih-Jhen Huang, Hou-Cheng Vong, Shih-Wei Wu (2013), "IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-10 RITE-2," in Proceedings of the 10th NTCIR Conference on Evaluation of Information Access Technologies(NTCIR-10), Tokyo, Japan, June 18-21, 2013, pp. 462-468.In this paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-10 RITE-2 (Recognizing Inference in Text). We proposed a textual entailment system using a hybrid approach that integrate semantic features and machine learning techniques for recognizing inference in text at NTCIR-10 RITE-2 task. We submitted 3 official runs for BC, MC and RITE4QA subtask. In NTCIR-10 RITE-2 task, IMTKU team achieved 0.509 in the CT-MC subtask, 0.663 in the CT-BC subtask; 0.402 in the CS-MC subtask, 0.627 in the CS-BC subtask; In MRR index, 0.257 in the CT-RITE4QA subtask, 0.338 in the CS-RITE4QA subtask.National Institute of Informatics (NII), Tokyo, Japan國際20130618~20130621電子版YTokyo, Japa
AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models
No full textChatBot technology has become a widely used in various application fields. An important topic in the research on conversational robots is the improvement of their temperature during operation for enhanced user interaction. In this study, we propose an artificial intelligence affective conversational robot (AIACR), which is an integration of an artificial intelligence deep learning sentiment analysis model and generative-and retrieval-based dialogue models. The sentiment analysis model developed in this study uses three models, namely, multilayer perceptron (MLP), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM). Moreover, word2vec and semantics are utilized as the basis for similarity ranking models. The deep learning dialogue model, sentiment analysis model, and similarity model were integrated and compared as well. The experimental results show that the sentiment analysis model, similarity model, and dialogue model respectively utilize BiLSTM, word2vec, and the retrieval-based model to achieve the best dialogue performance. The major research contributions of this study are the developed AIACR and the proposed affective conversational robot index (ACR Index) as a criterion for evaluating the effectiveness of emotional dialogue robots.補正完
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