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], "abstractNote": "Crowdsourcing is an emergent trend for general-purpose classification problem solving. Over the past decade, this notion has been embodied by enlisting a crowd of humans to help solve problems. There are a growing number of real-world problems that take advantage of this technique, such as Wikipedia, Linux or Amazon Mechanical Turk. In this paper, we evaluate its suitability for classification, namely if it can outperform state-of-the-art models by combining it with active learning techniques. We propose two approaches based on crowdsourcing and active learning and empirically evaluate the performance of a baseline Support Vector Machine when active learning examples are chosen and made available for classification to a crowd in a web-based scenario. The proposed crowdsourcing active learning approach was tested with Jester data set, a text humour classification benchmark, resulting in promising improvements over baseline results.", "proceedingsTitle": "Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on", "conferenceName": "", "publisher": "", "place": "", "date": "nov. 2011", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "469 -474", "series": "", "seriesNumber": "", "DOI": "10.1109/ISDA.2011.6121700", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Amazon Mechanical Turk", "type": 1 }, { "tag": "Internet", "type": 1 }, { "tag": "Linux", "type": 1 }, { "tag": "Web-based scenario", "type": 1 }, { "tag": "Wikipedia", "type": 1 }, { "tag": "active learning", "type": 1 }, { "tag": "classification", "type": 1 }, { "tag": "classification performance", "type": 1 }, { "tag": "crowdsourcing", "type": 1 }, { "tag": "general-purpose classification problem solving", "type": 1 }, { "tag": "learning (artificial intelligence)", "type": 1 }, { "tag": "support vector machine", "type": 1 }, { "tag": "support vector machines", "type": 1 }, { "tag": "text humour classification benchmark", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-07-08T21:07:10Z", "dateModified": "2012-07-08T21:07:10Z" } }, { "key": "M8RFP9TK", "version": 1, "library": { "type": "group", "id": 82636, "name": "TESIS JUAN DIEGO LOPEZ", "links": { "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/82636/items/M8RFP9TK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez/items/M8RFP9TK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1066167, "username": "jdlvtulua", "name": "Juan Diego López Vargas", "links": { "alternate": { "href": "https://www.zotero.org/jdlvtulua", "type": "text/html" } } }, "creatorSummary": "Welinder and Perona", "parsedDate": "2010-06", "numChildren": 2 }, "data": { "key": "M8RFP9TK", "version": 1, "itemType": "conferencePaper", "title": "Online crowdsourcing: Rating annotators and obtaining cost-effective labels", "creators": [ { "creatorType": "author", "firstName": "P.", "lastName": "Welinder" }, { "creatorType": "author", "firstName": "P.", "lastName": "Perona" } ], "abstractNote": "Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing services like Amazon Mechanical Turk. How can one trust the labels obtained from such services? We propose a model of the labeling process which includes label uncertainty, as well a multi-dimensional measure of the annotators' ability. From the model we derive an online algorithm that estimates the most likely value of the labels and the annotator abilities. It finds and prioritizes experts when requesting labels, and actively excludes unreliable annotators. Based on labels already obtained, it dynamically chooses which images will be labeled next, and how many labels to request in order to achieve a desired level of confidence. Our algorithm is general and can handle binary, multi-valued, and continuous annotations (e.g. bounding boxes). Experiments on a dataset containing more than 50,000 labels show that our algorithm reduces the number of labels required, and thus the total cost of labeling, by a large factor while keeping error rates low on a variety of datasets.", "proceedingsTitle": "Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on", "conferenceName": "", "publisher": "", "place": "", "date": "june 2010", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "25 -32", "series": "", "seriesNumber": "", "DOI": "10.1109/CVPRW.2010.5543189", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Online crowdsourcing", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Amazon Mechanical Turk", "type": 1 }, { "tag": "Web services", "type": 1 }, { "tag": "cost effective label", "type": 1 }, { "tag": "costing", "type": 1 }, { "tag": "dataset labeling service", "type": 1 }, { "tag": "image classification", "type": 1 }, { "tag": "label uncertainty", "type": 1 }, { "tag": "multidimensional annotator ability measurement", "type": 1 }, { "tag": "multivalued continuous annotation", "type": 1 }, { "tag": "online crowdsourcing", "type": 1 }, { "tag": "outsourcing", "type": 1 }, { "tag": "rating annotator", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-07-08T21:07:10Z", "dateModified": "2012-07-08T21:07:10Z" } }, { "key": "97TJHHBP", "version": 1, "library": { "type": "group", "id": 82636, "name": "TESIS JUAN DIEGO LOPEZ", "links": { "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/82636/items/97TJHHBP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez/items/97TJHHBP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1066167, "username": "jdlvtulua", "name": "Juan Diego López Vargas", "links": { "alternate": { "href": "https://www.zotero.org/jdlvtulua", "type": "text/html" } } }, "creatorSummary": "Liu et al.", "parsedDate": "2010-12", "numChildren": 2 }, "data": { "key": "97TJHHBP", "version": 1, "itemType": "conferencePaper", "title": "A collective data generation method for speech language models", "creators": [ { "creatorType": "author", "firstName": "S.", "lastName": "Liu" }, { "creatorType": "author", "firstName": "S.", "lastName": "Seneff" }, { "creatorType": "author", "firstName": "J.", "lastName": "Glass" } ], "abstractNote": "Recently we began using Amazon Mechanical Turk (AMT), an Internet marketplace, to deploy our spoken dialogue systems to large audiences for user testing and data collection purposes. This crowdsourcing method of collecting data contrasts with the time- and labor- intensive developer annotation methods. In this paper, we compare these data in various combinations with traditionally-collected corpora for training our speech recognizer's language model. Our results show that AMT text queries are effective for initial language model training for spoken dialogue systems, and that crowd-sourced speech collection within the context of a spoken dialogue framework provides significant improvement.", "proceedingsTitle": "Spoken Language Technology Workshop (SLT), 2010 IEEE", "conferenceName": "", "publisher": "", "place": "", "date": "dec. 2010", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "223 -228", "series": "", "seriesNumber": "", "DOI": "10.1109/SLT.2010.5700855", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Amazon Mechanical Turk", "type": 1 }, { "tag": "collective data generation method", "type": 1 }, { "tag": "crowdsourcing method", "type": 1 }, { "tag": "data handling", "type": 1 }, { "tag": "interactive systems", "type": 1 }, { "tag": "speech language model", "type": 1 }, { "tag": "speech recognition", "type": 1 }, { "tag": "speech recognizer language model", "type": 1 }, { "tag": "spoken dialogue systems", "type": 1 }, { "tag": "time-and labor-intensive developer annotation method", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-07-08T21:07:10Z", "dateModified": "2012-07-08T21:07:10Z" } }, { "key": "53Z36W4R", "version": 1, "library": { "type": "group", "id": 82636, "name": "TESIS JUAN DIEGO LOPEZ", "links": { "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/82636/items/53Z36W4R", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez/items/53Z36W4R", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1066167, "username": "jdlvtulua", "name": "Juan Diego López Vargas", "links": { "alternate": { "href": "https://www.zotero.org/jdlvtulua", "type": "text/html" } } }, "creatorSummary": "Gryc et al.", "parsedDate": "2010-04", "numChildren": 2 }, "data": { "key": "53Z36W4R", "version": 1, "itemType": "conferencePaper", "title": "A machine-learning approach to discovering company home pages", "creators": [ { "creatorType": "author", "firstName": "W.", "lastName": "Gryc" }, { "creatorType": "author", "firstName": "P.", "lastName": "Melville" }, { "creatorType": "author", "firstName": "R.D.", "lastName": "Lawrence" } ], "abstractNote": "For many marketing and business applications, it is necessary to know the home page of a company specified only by its company name. If we require the home page for a small number of big companies, this task is readily accomplished via use of Internet search engines or access to domain registration lists. However, if the entities of interest are small companies, these approaches can lead to mismatches, particularly if a specified company lacks a home page. We address this problem using a supervised machine-learning approach in which we train a binary classification model. We classify potential website matches for each company name based on a set of explanatory features extracted from the content on each candidate website. Our approach is related to web-based business intelligence in two ways: (1) we build the training set for our learning algorithms through crowdsourcing tools and illustrate their potential for business research, and (2) the success of our model allows one to easily use corporate home pages as data inputs into other research projects. Through the successful use of crowdsourcing, our approach is able to identify a correct home page or recognize that a valid home page does not exist with an accuracy that is 57% better than simply taking the highest ranked search engine result as the correct match.", "proceedingsTitle": "Digital Ecosystems and Technologies (DEST), 2010 4th IEEE International Conference on", "conferenceName": "", "publisher": "", "place": "", "date": "april 2010", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "361 -366", "series": "", "seriesNumber": "", "DOI": "10.1109/DEST.2010.5610621", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Internet search engines", "type": 1 }, { "tag": "Web site", "type": 1 }, { "tag": "Web sites", "type": 1 }, { "tag": "Web-based business intelligence", "type": 1 }, { "tag": "binary classification model", "type": 1 }, { "tag": "business application", "type": 1 }, { "tag": "business data processing", "type": 1 }, { "tag": "company home pages discovering", "type": 1 }, { "tag": "crowdsourcing tools", "type": 1 }, { "tag": "explanatory features extraction", "type": 1 }, { "tag": "feature extraction", "type": 1 }, { "tag": "learning (artificial intelligence)", "type": 1 }, { "tag": "learning algorithm", "type": 1 }, { "tag": "marketing application", "type": 1 }, { "tag": "pattern classification", "type": 1 }, { "tag": "search engines", "type": 1 }, { "tag": "supervised machine-learning approach", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-07-08T21:07:10Z", "dateModified": "2012-07-08T21:07:10Z" } }, { "key": "CTTH84DN", "version": 1, "library": { "type": "group", "id": 82636, "name": "TESIS JUAN DIEGO LOPEZ", "links": { "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/82636/items/CTTH84DN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/tesis_juan_diego_lopez/items/CTTH84DN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1066167, "username": "jdlvtulua", "name": "Juan Diego López Vargas", "links": { "alternate": { "href": "https://www.zotero.org/jdlvtulua", "type": "text/html" } } }, "creatorSummary": "Yang et al.", "parsedDate": "2010-12", "numChildren": 2 }, "data": { "key": "CTTH84DN", "version": 1, "itemType": "conferencePaper", "title": "Collaborative filtering model for user satisfaction prediction in Spoken Dialog System evaluation", "creators": [ { "creatorType": "author", "firstName": "Zhaojun", "lastName": "Yang" }, { "creatorType": "author", "firstName": "Baichuan", "lastName": "Li" }, { "creatorType": "author", "firstName": "Yi", "lastName": "Zhu" }, { "creatorType": "author", "firstName": "I.", "lastName": "King" }, { "creatorType": "author", "firstName": "G.", "lastName": "Levow" }, { "creatorType": "author", "firstName": "H.", "lastName": "Meng" } ], "abstractNote": "Developing accurate models to automatically predict user satisfaction about the overall quality of a Spoken Dialog System (SDS) is highly desirable for SDS evaluation. In the original PARADISE framework, a linear regression model is trained using measures drawn from rated dialogs as predictors with user satisfaction as the target. In this paper, we extend PARADISE by introducing a collaborative filtering (CF) model for user satisfaction prediction and its corresponding extension. This prediction model is drawn from the idea of CF in recommendation systems, which uses information from near neighbors of an unrated dialog to predict its user satisfaction. We also present the methodology of collecting user judgments on SDS quality with crowdsourcing through Amazon Mechanical Turk. 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