Xem 1-20 trên 106 kết quả Bootstrapping
  • In this paper, we present an unsupervised framework that bootstraps a complete coreference resolution (CoRe) system from word associations mined from a large unlabeled corpus. We show that word associations are useful for CoRe – e.g., the strong association between Obama and President is an indicator of likely coreference. Association information has so far not been used in CoRe because it is sparse and difficult to learn from small labeled corpora.

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  • In bootstrapping (seed set expansion), selecting good seeds and creating stop lists are two effective ways to reduce semantic drift, but these methods generally need human supervision. In this paper, we propose a graphbased approach to helping editors choose effective seeds and stop list instances, applicable to Pantel and Pennacchiotti’s Espresso bootstrapping algorithm. The idea is to select seeds and create a stop list using the rankings of instances and patterns computed by Kleinberg’s HITS algorithm. ...

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  • State-of-the-art bootstrapping systems rely on expert-crafted semantic constraints such as negative categories to reduce semantic drift. Unfortunately, their use introduces a substantial amount of supervised knowledge. We present the Relation Guided Bootstrapping (RGB) algorithm, which simultaneously extracts lexicons and open relationships to guide lexicon growth and reduce semantic drift. This removes the necessity for manually crafting category and relationship constraints, and manually generating negative categories. ...

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  • We investigate the unsupervised detection of semi-fixed cue phrases such as “This paper proposes a novel approach. . . 1 ” from unseen text, on the basis of only a handful of seed cue phrases with the desired semantics. The problem, in contrast to bootstrapping approaches for Question Answering and Information Extraction, is that it is hard to find a constraining context for occurrences of semi-fixed cue phrases. Our method uses components of the cue phrase itself, rather than external context, to bootstrap. ...

    pdf8p hongvang_1 16-04-2013 14 2   Download

  • This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE type, e.g. he/she/man/woman for PERSON NE. The entire bootstrapping procedure is implemented as training two successive learners: (i) a decision list is used to learn the parsing-based high precision NE rules; (ii) a Hidden Markov Model is then trained to learn string sequence-based NE patterns.

    pdf8p bunbo_1 17-04-2013 13 2   Download

  • Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requires minimal human supervision.

    pdf10p bunthai_1 06-05-2013 18 2   Download

  • In this paper, we describe a new approach to semi-supervised adaptive learning of event extraction from text. Given a set of examples and an un-annotated text corpus, the BEAR system (Bootstrapping Events And Relations) will automatically learn how to recognize and understand descriptions of complex semantic relationships in text, such as events involving multiple entities and their roles. For example, given a series of descriptions of bombing and shooting incidents (e.g.

    pdf10p bunthai_1 06-05-2013 22 2   Download

  • Bootstrapping a classifier from a small set of seed rules can be viewed as the propagation of labels between examples via features shared between them. This paper introduces a novel variant of the Yarowsky algorithm based on this view. It is a bootstrapping learning method which uses a graph propagation algorithm with a well defined objective function.

    pdf9p nghetay_1 07-04-2013 13 1   Download

  • We address two problems in the field of automatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating the result with real users. We use Reinforcement Learning (RL) to learn multimodal dialogue strategies by interaction with a simulated environment which is “bootstrapped” from small amounts of Wizard-of-Oz (WOZ) data.

    pdf9p hongphan_1 15-04-2013 22 1   Download

  • We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. Highly coreferent paths also allow mining of precise probabilistic gender/number information. ...

    pdf8p hongvang_1 16-04-2013 23 1   Download

  • We introduce a simple method to pack words for statistical word alignment. Our goal is to simplify the task of automatic word alignment by packing several consecutive words together when we believe they correspond to a single word in the opposite language. This is done using the word aligner itself, i.e. by bootstrapping on its output. We evaluate the performance of our approach on a Chinese-to-English machine translation task, and report a 12.2% relative increase in BLEU score over a state-of-the art phrasebased SMT system. ...

    pdf8p hongvang_1 16-04-2013 14 1   Download

  • We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The transducer learns its similarity metric from the data in the bitext, and thus can function directly on strings written in different writing scripts without any additional language knowledge. We show that this bootstrapped transducer performs as well or better than a model designed specifically to detect Arabic-English transliterations. ...

    pdf8p hongvang_1 16-04-2013 19 1   Download

  • A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while gathering a large quantity of unlabeled data is cheap. We here propose a new automatic text categorization method for learning from only unlabeled data using a bootstrapping framework and a feature projection technique.

    pdf8p bunbo_1 17-04-2013 19 1   Download

  • This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual bootstrapping, which are referred to, in a general term, as ‘collaborative bootstrapping’. The paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping.

    pdf8p bunbo_1 17-04-2013 25 1   Download

  • This paper proposes a new method for word translation disambiguation using a machine learning technique called ‘Bilingual Bootstrapping’. Bilingual Bootstrapping makes use of in learning a small number of classified data and a large number of unclassified data in the source and the target languages in translation. It constructs classifiers in the two languages in parallel and repeatedly boosts the performances of the classifiers by further classifying data in each of the two languages and by exchanging between the two languages information regarding the classified data. ...

    pdf9p bunmoc_1 20-04-2013 18 1   Download

  • We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material.

    pdf8p bunthai_1 06-05-2013 16 1   Download

  • Author David Cochran Reviewers Chris Gunther Veturi JV Subramanyeswari Production Coordinator Acquisition Editor Sarah Cullington Cover Work Commissioning Editor Meeta Rajani Technical Editor Vrinda Amberkar Melwyn D'sa Melwyn D'sa Project Coordinator Michelle Quadros Proofreader Maria Gould

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  • Trong bài báo này, chúng tôi sử dụng phương pháp bootstrap để nghiên cứu độ lệch tiêu chuẩn của mật độ xương tối đa của phụ nữ Việt Nam. Kết quả này có tầm quan trọng trong việc nhận biết mức độ nguy hiểm của căn bệnh loãng xương.

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  • Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học tạp chí Department of Mathematic dành cho các bạn yêu thích môn toán học đề tài: Bootstrap Percolation and Diffusion in Random Graphs with Given Vertex Degrees...

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  • Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học tạp chí Department of Mathematic dành cho các bạn yêu thích môn toán học đề tài: Largest minimal percolating sets in hypercubes under 2-bootstrap percolation...

    pdf13p thulanh7 04-10-2011 21 3   Download


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