Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1107)
- TÜBİTAK (199)
- Wright State University (191)
- Technological University Dublin (182)
- Old Dominion University (126)
-
- Missouri University of Science and Technology (112)
- San Jose State University (111)
- City University of New York (CUNY) (92)
- Neutrosophic Systems with Applications (89)
- MBZUAI (70)
- Dartmouth College (67)
- Brigham Young University (65)
- University of Texas at El Paso (63)
- Chulalongkorn University (62)
- Zayed University (58)
- University of Nebraska - Lincoln (54)
- Kennesaw State University (49)
- Boise State University (48)
- Edith Cowan University (48)
- New Jersey Institute of Technology (44)
- California Polytechnic State University, San Luis Obispo (43)
- Embry-Riddle Aeronautical University (42)
- Purdue University (41)
- Chapman University (40)
- University of Nebraska at Omaha (40)
- University of Arkansas, Fayetteville (38)
- Montclair State University (36)
- University of South Florida (36)
- Air Force Institute of Technology (35)
- MMU Press (35)
- Keyword
-
- Machine learning (184)
- Natural language processing (182)
- Artificial intelligence (140)
- Natural Language Processing (131)
- Machine Learning (125)
-
- Deep learning (118)
- Artificial Intelligence (94)
- Large language models (84)
- Social media (80)
- Computer Science (78)
- Twitter (64)
- Deep Learning (61)
- NLP (60)
- Sentiment analysis (59)
- Large Language Models (56)
- Computational linguistics (53)
- Department of Computer Science and Engineering (53)
- Text mining (51)
- Neural networks (48)
- Data mining (44)
- Generative AI (44)
- AI (42)
- Computer science (39)
- Classification (37)
- Information retrieval (36)
- Social Media (36)
- Fuzzy logic (35)
- Semantics (34)
- Sentiment Analysis (33)
- LLM (32)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1032)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Theses and Dissertations (160)
- Master's Projects (99)
- Conference papers (93)
-
- Neutrosophic Systems with Applications (89)
- Kno.e.sis Publications (84)
- Dissertations (70)
- Computer Science Faculty Research & Creative Works (66)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (62)
- Browse all Theses and Dissertations (61)
- All Works (58)
- Computer Science Faculty Publications (58)
- Faculty Publications (49)
- Computer Science Faculty Publications and Presentations (45)
- Publications and Research (44)
- Articles (40)
- Electronic Theses and Dissertations (39)
- Computer Science and Engineering Faculty Publications (38)
- Natural Language Processing Faculty Publications (38)
- Departmental Technical Reports (CS) (36)
- Journal of Informatics and Web Engineering (35)
- Dissertations and Theses Collection (Open Access) (34)
- USF Tampa Graduate Theses and Dissertations (34)
- Computer Science: Faculty Publications (33)
- Dissertations, Theses, and Capstone Projects (32)
- Theses (31)
- CCAC Theses and Dissertations (30)
- Faculty Scholarship (27)
- Masters Theses (26)
- Publication Type
- File Type
Articles 3841 - 3870 of 4473
Full-Text Articles in Entire DC Network
Evolutionary Strategies For Data Mining, Rose Lowe
Evolutionary Strategies For Data Mining, Rose Lowe
All Dissertations
Learning classifier systems (LCS) have been successful in generating rules for solving classification problems in data mining. The rules are of the form IF condition THEN action. The condition encodes the features of the input space and the action encodes the class label. What is lacking in those systems is the ability to express each feature using a function that is appropriate for that feature. The genetic algorithm is capable of doing this but cannot because only one type of membership function
is provided. Thus, the genetic algorithm learns only the shape and placement of the membership function, and in …
Organisational Resilience: A Propositional Study To Understand And Identify The Essential Concepts, Bruce Braes, David Brooks
Organisational Resilience: A Propositional Study To Understand And Identify The Essential Concepts, Bruce Braes, David Brooks
Australian Security and Intelligence Conference
Increased exposure to turmoil has raised business, social and governmental concerns over the ability of organisations to anticipate and respond positively to disruptions. Organisations are spending increased sums of money to raise levels of security; however, Organisational Resilience is a vague, multidisciplined and diverse philosophy, requiring a multiplicity of skills and knowledge that reaches far beyond security alone. The resilience domain is still developing and expanding; however, early embodiments of Organisational Resilience, originating in the United Kingdom and the United States, were nothing more than a rebranding of business continuity management strategies, put together as a ‘resilience processes’, or ‘resilience …
Anomaly Detection Over User Profiles For Intrusion Detection, Grant Pannell, Helen Ashman
Anomaly Detection Over User Profiles For Intrusion Detection, Grant Pannell, Helen Ashman
Australian Information Security Management Conference
Intrusion detection systems (IDS) have often been used to analyse network traffic to help network administrators quickly identify and respond to intrusions. These detection systems generally operate over the entire network, identifying “anomalies” atypical of the network’s normal collective user activities. We show that anomaly detection could also be host-based so that the normal usage patterns of an individual user could be profiled. This enables the detection of masquerading intruders by comparing a learned user profile against the current session’s profile. A prototype behavioural IDS applies the concept of anomaly detection to user behaviour and compares the effects of using …
Web-Based Intelligent Computational Argumentation Based Conflict Resolution In Air Traffic Management, Xiaoqing Frank Liu, Ravi Santosh Arvapally, Hojong Baik, Rubal Wanchoo
Web-Based Intelligent Computational Argumentation Based Conflict Resolution In Air Traffic Management, Xiaoqing Frank Liu, Ravi Santosh Arvapally, Hojong Baik, Rubal Wanchoo
Computer Science Faculty Research & Creative Works
Collaborative decision making (CDM) is a process of reaching consensus on a potential solution of an issue through the evaluation of the different possible alternatives. the web-Based intelligent computational argumentation system allows concerned stakeholders to post their arguments on different alternatives, assign weights and priorities to the arguments and reach the most favorable alternative using fuzzy intelligent techniques over the Internet. Exchange of information among the stakeholders improves the collaboration and drives towards the collective intelligence. in this paper, we show how our tool facilitates resolution of conflicts in air traffic management. It could enhance the Ground Delay Program (GDP) …
Constructive Ontology Engineering, William L. Sousan
Constructive Ontology Engineering, William L. Sousan
Student Work
The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated …
Flexible Bootstrapping-Based Ontology Alignment, Prateek Jain, Pascal Hitzler, Amit P. Sheth
Flexible Bootstrapping-Based Ontology Alignment, Prateek Jain, Pascal Hitzler, Amit P. Sheth
Kno.e.sis Publications
BLOOMS (Jain et al, ISWC2010) is an ontology alignment system which, in its core, utilizes the Wikipedia category hierarchy for establishing alignments. In this paper, we present a Plug-and-Play extension to BLOOMS, which allows to flexibly replace or complement the use of Wikipedia by other online or offline resources, including domain-specific ontologies or taxonomies. By making use of automated translation services and of Wikipedia in languages other than English, it makes it possible to apply BLOOMS to alignment tasks where the input ontologies are written in different languages.
Smu-Sis At Tac 2010 - Kbp Track Entity Linking, Swapna Gottipati, Jing Jiang
Smu-Sis At Tac 2010 - Kbp Track Entity Linking, Swapna Gottipati, Jing Jiang
Research Collection School Of Computing and Information Systems
Entity linking task is a process of linking the named entity within the unstructured text to the entity in the Knowledge Base. Entity liking to the relevant knowledge is useful in various information extraction and natural language processing applications that improve the user experiences such as search, summarization and so on. We propose the two way entity linking approach to reformulate query, disambiguate the entity and link to the relevant KB repository. This paper describes the details of our participation in TAC 2010 - Knowledge Base Population track. We provided an innovative approach to disambiguate the entity by query reformulation …
Visual Salience And Reference Resolution In Situated Dialogues: A Corpus-Based Evaluation., Niels Schütte, John D. Kelleher, Brian Mac Namee
Visual Salience And Reference Resolution In Situated Dialogues: A Corpus-Based Evaluation., Niels Schütte, John D. Kelleher, Brian Mac Namee
Conference papers
Dialogues between humans and robots are necessarily situated and so, often, a shared visual context is present. Exophoric references are very frequent in situated dialogues, and are particularly important in the presence of a shared visual context - for example when a human is verbally guiding a tele-operated mobile robot. We present an approach to automatically resolving exophoric referring expressions in a situated dialogue based on the visual salience of possible referents. We evaluate the effectiveness of this approach and a range of different salience metrics using data from the SCARE corpus which we have augmented with visual information. The …
Situating Spatial Templates For Human-Robot Interaction, John D. Kelleher, Robert J. Ross, Brian Mac Namee, Colm Sloan
Situating Spatial Templates For Human-Robot Interaction, John D. Kelleher, Robert J. Ross, Brian Mac Namee, Colm Sloan
Conference papers
People often refer to objects by describing the object's spatial location relative to another object. Due to their ubiquity in situated discourse, the ability to use 'locative expressions' is fundamental to human-robot dialogue systems. A key component of this ability are computational models of spatial term semantics. These models bridge the grounding gap between spatial language and sensor data. Within the Artificial Intelligence and Robotics communities, spatial template based accounts, such as the Attention Vector Sum model (Regier and Carlson, 2001), have found considerable application in mediating situated human-machine communication (Gorniak, 2004; Brenner et a., 2007; Kelleher and Costello, 2009). …
Automatic Classification Of Article Errors In L2 Written English, Aliva M. Pradhan, Aparna S. Varde, Jing Peng, Eileen M. Fitzpatrick
Automatic Classification Of Article Errors In L2 Written English, Aliva M. Pradhan, Aparna S. Varde, Jing Peng, Eileen M. Fitzpatrick
Department of Computer Science Faculty Scholarship and Creative Works
This paper presents an approach to the automatic classification of article errors in non-native (L2) English writing, using data chosen from the MELD corpus that was purposely selected to contain only cases with article errors. We report on two experiments on the data: one to assess the performance of different machine learning algorithms in predicting correct article usage, and the other to determine the feasibility of using the MELD data to identify which linguistic properties of the noun phrase containing the article are the most salient with respect to the classification of errors in article usage. Copyright © 2010, Association …
Youth Olympic Village Co-Space, Zin-Yan Chua, Yilin Kang, Xing Jiang, Kah-Hoe Pang, Andrew C. Gregory, Chi-Yun Tan, Wai-Lun Wong, Ah-Hwee Tan, Yew-Soon Ong, Chunyan Miao
Youth Olympic Village Co-Space, Zin-Yan Chua, Yilin Kang, Xing Jiang, Kah-Hoe Pang, Andrew C. Gregory, Chi-Yun Tan, Wai-Lun Wong, Ah-Hwee Tan, Yew-Soon Ong, Chunyan Miao
Research Collection School Of Computing and Information Systems
We have designed and implemented a 3D virtual world based on the Co-Space concept encompasses the Youth Olympic Village (YOV) and several sports competition venues. It is a massively multiplayer online (MMO) virtual world built according to the actual, physical locations of the YOV and sports competition venues. On top of that, the Co-Space is being populated with human-like avatars, which are created according to the actual human size and appearance; they perform their activities and interact with the users in realworld context. In addition, autonomous intelligent agents are integrated into the Co-Space to provide context-aware and personalized services to …
Jointly Modeling Aspects And Opinions With A Maxent-Lda Hybrid, Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
Jointly Modeling Aspects And Opinions With A Maxent-Lda Hybrid, Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and opinions. Recently topic models have been used to identify meaningful review aspects, but existing topic models do not identify aspect-specific opinion words. In this paper, we propose a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words. We show that with a relatively small amount of training data, our model can effectively identify aspect and opinion words simultaneously. We also demonstrate the domain adaptability of our model.
Electroencephalogram Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen
Electroencephalogram Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen
Electrical & Computer Engineering Theses & Dissertations
A wavelet neural network (WNN) technique rs developed for electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this thesis is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. To remove EEG ai1ifacts, a WNN tries to learn the characteristics of the artifacts first and does not need reference EOG signals once trained. Second, WNNs are …
On Challenges In Evaluating Malware Clustering, Peng Li, Limin Liu, Debin Gao, Michael K Reiter
On Challenges In Evaluating Malware Clustering, Peng Li, Limin Liu, Debin Gao, Michael K Reiter
Research Collection School Of Computing and Information Systems
Malware clustering and classification are important tools that enable analysts to prioritize their malware analysis efforts. The recent emergence of fully automated methods for malware clustering and classification that report high accuracy suggests that this problem may largely be solved. In this paper, we report the results of our attempt to confirm our conjecture that the method of selecting ground-truth data in prior evaluations biases their results toward high accuracy. To examine this conjecture, we apply clustering algorithms from a different domain (plagiarism detection), first to the dataset used in a prior work's evaluation and then to a wholly new …
Increasing Effectiveness Of The Zachman Framework Using The Balanced Scorecard, Anagha Gokhale
Increasing Effectiveness Of The Zachman Framework Using The Balanced Scorecard, Anagha Gokhale
Purdue Polytechnic Masters Theses
The purpose of this study is to explore the impact of integrating the use of Zachman Framework of Enterprise Architecture and the Balanced Scorecard (BSC) Framework for an effective business-IT alignment. The study tries to identify certain gaps in the Zachman Framework focusing on the motivational aspects of the framework, which have been discussed in the literature review. The aim is to achieve the integration by mitigating these motivational aspect’s weaknesses in the Zachman Framework using the deliverables obtained from the BSC. Thus the author proposes to achieve business-IT alignment through this integration. No research studies in the past have …
Investigating Capabilities Associated With Ict Access And Use In Latino Micro-Enterprises, Travis Good, Luis Flores Morales, Sajda Qureshi
Investigating Capabilities Associated With Ict Access And Use In Latino Micro-Enterprises, Travis Good, Luis Flores Morales, Sajda Qureshi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
While the process by which Information Technology enables growth in medium and large enterprises has been wellresearched, the corresponding processes in micro-enterprises are poorly understood. In fact, such micro-enterprises lie at the heart of many economies. This insight is important as information technology enables businesses to connect with each other through knowledge networking to carry out their basic business operations. There is thus a need to build our understanding of how micro-enterprises access and use technology in order to be able to assess the benefits they derive from ICT adoption. Following an analysis of two case studies of Latino micro-enterprises …
Cross-Market Model Adaptation With Pairwise Preference Data For Web Search Ranking, Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen
Cross-Market Model Adaptation With Pairwise Preference Data For Web Search Ranking, Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen
Kno.e.sis Publications
Machine-learned ranking techniques automatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibility required of a commercial web search. However, manually labeled training data (with multiple absolute grades) has become the bottleneck for training a quality ranking function, particularly for a new domain. In this paper, we explore the adaptation of machine-learned ranking models across a set of geographically diverse markets with the market-specific pairwise preference data, which can be easily obtained from clickthrough logs. We propose a novel adaptation algorithm, Pairwise-Trada, which is able to adapt ranking models that are trained …
Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang
Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang
Research Collection School Of Computing and Information Systems
The overwhelming amounts of multimedia contents have triggered the need for automatically detecting the semantic concepts within the media contents. With the development of photo sharing websites such as Flickr, we are able to obtain millions of images with usersupplied tags. However, user tags tend to be noisy, ambiguous and incomplete. In order to improve the quality of tags to annotate web images, we propose an approach to build Semantic Fields for annotating the web images. The main idea is that the images are more likely to be relevant to a given concept, if several tags to the image belong …
Topology In Composite Spatial Terms, John D. Kelleher, Robert J. Ross
Topology In Composite Spatial Terms, John D. Kelleher, Robert J. Ross
Conference papers
People often refer to objects by describing the object's spatial location relative to another object, e.g. the book on the right of the table. This type of referring expression is called a spatial locative expression. Spatial locatives have three major components: (1) the target object that is being located (the book), (2) the landmark object relative to which the target is being located (the table), and (3) the description of the spatial relationship that exists between the target and the landmark (on the right of ). In English spatial relationships are often described using spatial prepositions. The set of English …
Merging Schemas In A Collaborative Faceted Classification System, Jianxiang Li
Merging Schemas In A Collaborative Faceted Classification System, Jianxiang Li
Computer Science Theses & Dissertations
We have developed a system that improves access to a large, growing image collection by allowing users to collaboratively build a global faceted (multi-perspective) classification schema. We are extending our system to support both global and local schemas, where global schema provides a complete and uniform view of the collection whereas local schema provides a personal, possibly incomplete and idiosyncratic view of the collection. We argue that although users usually focus on their personal schemas, it is still desirable to have a global schema for the entire collection even if such local schemas are available. In order to keep the …
Intelligent Computational Argumentation For Evaluating Performance Scores In Multi-Criteria Decision Making, Xiaoqing Frank Liu, Rubal Wanchoo, Ravi Santosh Arvapally
Intelligent Computational Argumentation For Evaluating Performance Scores In Multi-Criteria Decision Making, Xiaoqing Frank Liu, Rubal Wanchoo, Ravi Santosh Arvapally
Computer Science Faculty Research & Creative Works
Multi-Criteria decision making (MCDM), is a discipline aimed at assisting multiple stakeholders in contemplating a decision paradigm in an uncertain environment. the decision analysis to be performed involves numerous alternative positions assessed under varied criterion. a performance score is assigned for each alternative in terms of every criterion, and it represents satisfaction of the criteria by that alternative. in real applications, performance scores are sometimes hard to determine, and they are often subjective. We have developed an intelligent computational argumentation approach for dealing with the problem of uncertainty in resolving the subjective scores. in this approach, an argumentation tree is …
Intelligent Time-Aware Query Translation For Text Sources, Amal Kaluarachchi, Aparna S. Varde, Jing Peng, Anna Feldman
Intelligent Time-Aware Query Translation For Text Sources, Amal Kaluarachchi, Aparna S. Varde, Jing Peng, Anna Feldman
Department of Computer Science Faculty Scholarship and Creative Works
Time-stamped documents such as newswire articles, blog posts and other web-pages are often archived online. Since these archives cover long spans of time, the terminology in them could undergo significant evolution. In answering user queries over such text, it is desirable that the system be intelligent enough to incorporate historical information. For example, a query on Sri Lanka should automatically retrieve documents with its former name Ceylon. Hence, temporal terminology evolution needs to be taken into account to translate these queries. This has become vital today because users expect that computer systems have the intelligence to find all related information …
A Probabilistic Morphological Analyzer For Syriac, Peter J. Mcclanahan
A Probabilistic Morphological Analyzer For Syriac, Peter J. Mcclanahan
Theses and Dissertations
We show that a carefully crafted probabilistic morphological analyzer significantly outperforms a reasonable, naive baseline for Syriac. Syriac is an under-resourced Semitic language for which there are no available language tools such as morphological analyzers. Such tools are widely used to contribute to the process of annotating morphologically complex languages. We introduce and connect novel data-driven models for segmentation, dictionary linkage, and morphological tagging in a joint pipeline to create a probabilistic morphological analyzer requiring only labeled data. We explore the performance of this model with varying amounts of training data and find that with about 34,500 tokens, it can …
Robust Fuzzy Cpu Utilization Control For Dynamic Workloads, Can Basaran, Mehmet H. Suzer, Kyoung-Don Kang, Xue Liu
Robust Fuzzy Cpu Utilization Control For Dynamic Workloads, Can Basaran, Mehmet H. Suzer, Kyoung-Don Kang, Xue Liu
Computer Science Faculty Scholarship
In a number of real-time applications such as target tracking, precise workloads are unknown a priori but may dynamically vary, for example, based on the changing number of targets to track. It is important to manage the CPU utilization, via feedback control, to avoid severe overload or underutilization even in the presence of dynamic workloads. However, it is challenge to model a real-time system for feedback control, as computer systems cannot be modeled via physics laws. In this paper, we present a novel closed-loop approach for utilization control based on formal fuzzy logic control theory, which is very effective to …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We …
Proceedings Of The Sixth International Natural Language Generation Conference (Inlg 2010)., John D. Kelleher, Brian Mac Namee, Ielka Van Der Sluis
Proceedings Of The Sixth International Natural Language Generation Conference (Inlg 2010)., John D. Kelleher, Brian Mac Namee, Ielka Van Der Sluis
Conference papers
No abstract provided.
Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou
Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou
Research Collection School Of Computing and Information Systems
Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise …
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …