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1,733
Learning realistic human actions from movies
- IN: CVPR.
, 2008
"... The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribut ..."
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Cited by 738 (48 self)
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The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multichannel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8 % accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.
RCV1: A new benchmark collection for text categorization research
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2004
"... Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data ..."
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Cited by 663 (11 self)
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Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection’s properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well as
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
- In Proceedings of the 20th International Joint Conference on Artificial Intelligence
, 2007
"... Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedi ..."
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Cited by 562 (9 self)
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Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r =0.56 to 0.75 for individual words and from r =0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users. 1
From frequency to meaning : Vector space models of semantics
- Journal of Artificial Intelligence Research
, 2010
"... Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are begi ..."
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Cited by 347 (3 self)
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Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term–document, word–context, and pair–pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field. 1.
Who should fix this bug?
- ICSE'06
, 2006
"... Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the respo ..."
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Cited by 240 (8 self)
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Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open source developments are burdened by the rate at which new bug reports appear in the bug repository. In this paper, we present a semi-automated approach intended to ease one part of this process, the assignment of reports to a developer. Our approach applies a machine learning algorithm to the open bug repository to learn the kinds of reports each developer resolves. When a new report arrives, the classifier produced by the machine learning technique suggests a small number of developers suitable to resolve the report. With this approach, we have reached precision levels of 57 % and 64 % on the Eclipse and Firefox development projects respectively. We have also applied our approach to the gcc open source development with less positive results. We describe the conditions under which the approach is applicable and also report on the lessons we learned about applying machine learning to repositories used in open source development.
ML-KNN: A lazy learning approach to multi-label learning
- Pattern Recognition
"... Abstract: Multi-label learning originated from the investigation of text cat-egorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to pr ..."
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Cited by 184 (21 self)
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Abstract: Multi-label learning originated from the investigation of text cat-egorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the la-bel sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named Ml-knn is presented, which is derived from the traditional k-Nearest Neighbor (kNN) algorithm. In detail, for each unseen instance, its k nearest neighbors in the training set are firstly identified. After that, based on statistical in-formation gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learn-ing problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show thatMl-knn achieves superior performance to some well-established multi-label learning algorithms.
A Survey of Modern Authorship Attribution Methods
- JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
"... Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed ..."
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Cited by 170 (9 self)
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Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.
Overview of record linkage and current research directions
- BUREAU OF THE CENSUS
, 2006
"... This paper provides background on record linkage methods that can be used in combining data from a variety of sources such as person lists business lists. It also gives some areas of current research. ..."
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Cited by 139 (1 self)
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This paper provides background on record linkage methods that can be used in combining data from a variety of sources such as person lists business lists. It also gives some areas of current research.
Mining the Biomedical Literature in the Genomic Era: An Overview
- JOURNAL OF COMPUTATIONAL BIOLOGY
, 2003
"... The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last f ..."
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Cited by 132 (5 self)
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The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years there is a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature, and find the nuggets of information most relevant and useful for specific analysis tasks. This paper
Overcoming the brittleness bottleneck using wikipedia: enhancing text categorization with encyclopedic knowledge
- In National Conference on Artificial Intelligence (AAAI
, 2006
"... When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle—they traditionally represent document ..."
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Cited by 127 (7 self)
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When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle—they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence “Wal-Mart supply chain goes real time”, how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that “Ciprofloxacin belongs to the quinolones group”, how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge—an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.
