Analysis of Identifying Linguistic Phenomena for Recognizing Inference in Text

[[abstract]]Recognizing Textual Entailment (RTE) is a task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. Although a considerable number of studies have been made on recognizing textual entailment, little is known about the power of linguistic phenomenon for recognizing inference in text. The objective of this paper is to provide a comprehensive analysis of identifying linguistic phenomena for recognizing inference in text (RITE). In this paper, we focus on RITE-VAL System Validation subtask and propose a model by using an analysis of identifying linguistic phenomena for Recognizing Inference in Text (RITE) using the development dataset of NTCIR-11 RITE-VAL subtask. The experimental results suggest that well identified linguistic phenomenon category could enhance the accuracy of textual entailment system.[[sponsorship]]IEEE[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140813~20140815[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, California, US

IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-10 RITE-2

Min-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.[[abstract]]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.[[sponsorship]]National Institute of Informatics (NII), Tokyo, Japan[[conferencetype]]國際[[conferencedate]]20130618~20130621[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-11

[[abstract]]In this paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-11 RITE-VAL (Recognizing Inference in Text). We proposed a textual entailment system using statistics approach that integrate semantic features and machine learning techniques for recognizing inference in text at NTCIR-11 RITEVAL task. We submitted 3 official runs for BC, MC subtask. In NTCIR-11 RITE-VAL task, IMTKU team achieved 0.2911 in the CT-MC subtask, 0.5275 in the CT-BC subtask; 0.2917 in the CSMC subtask, 0.5325 in the CS-BC subtask.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20141209~20141212[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

[[alternative]]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

[[abstract]]By 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.[[notice]]補正完

Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis

Chun 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.[[abstract]]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.[[sponsorship]]IEEE[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20130814~20130816[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, US

A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment

[[abstract]]Recognizing Textual Entailment (RTE) is a PASCAL/TAC task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. In this paper, we proposed a textual entailment system using a statistical approach that integrates syntactic and semantic techniques for Recognizing Inference in Text (RITE) using the NTCIR-9 RITE task and make a comparison between semantic and syntactic features based on their differences. We thoroughly evaluate our approach using subtasks of the NTCIR-9 RITE. As a result, our system achieved 73.28% accuracy on the Chinese Binary-Class (BC) subtask with NTCIR-9 RITE. Thorough experiments with the text fragments provided by the NTCIR-9 RITE task show that the proposed approach can significantly improve system accuracy.[[sponsorship]]IEEE[[incitationindex]]EI[[cooperationtype]]國外[[conferencetype]]國際[[conferencedate]]20120808~20120810[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Vegas, Nevada, US

A Comparative Study of Data Mining Techniques for Credit Scoring in Banking

Shih-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.[[abstract]]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.[[sponsorship]]IEEE[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20130814~20130816[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, California, US

Exploring impacts of project leaders’ written expressions in virtual and fluid projects: The role of personality and emotion

This paper aims to tackle challenges of managing projects in highly virtual and fluid contexts, characterized by diversity, dispersion, digital dependence, unstable membership, and dynamic coordination and configuration. We investigate project leaders’ personality and emotion expressed in written expression and examine their impacts on collaboration outcomes. IBM Watson Personality Insights and Tone Analyzer were adopted to assess the leader’s personality and emotion. A computation model to classify collaboration patterns into taskwork-related and teamwork-related communication is under development. We report preliminary findings based on 417 weekly meetings between October 2018 and February 2020 in 8 open-source software teams around WordPress. The research results have the potential to inform researchers and practitioners about what personality profiles and emotions should be considered to foster collaboration in virtual and fluid projects. It is possible to extend the boundary condition of the traits school of leadership for project management in the new context

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe