Learning event

Xem 1-20 trên 128 kết quả Learning event
  • We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.

    pdf8p hongvang_1 16-04-2013 15 2   Download

  • Learn the JavaScript application structure, including basic statements and control structures Identify JavaScript objects -- String, Number, Boolean, Function, and more Use browser debugging tools and troubleshooting techniques Understand event handling, form events, and JavaScript applications with forms Develop with the Browser Object Model, the Document Object Model, and custom objects you create

    pdf405p trasua_123 05-01-2013 33 6   Download

  • FOR THE PAST SEVERAL DECADES instructors in the field of educational technology have focused on training instructional designers. A quick survey of graduate programs in this area indicates that the majority of the classes are geared toward instructional design from the viewpoint of learning psychology or from the viewpoint of using multimedia tools for the latest technological advances. Courses have emphasized a systematic approach to the development of instructional products usually consisting of various approaches to analysis, design, development, implementation, and evaluation.

    pdf301p japet75 30-01-2013 29 5   Download

  • Annotating training data for event extraction is tedious and labor-intensive. Most current event extraction tasks rely on hundreds of annotated documents, but this is often not enough. In this paper, we present a novel self-training strategy, which uses Information Retrieval (IR) to collect a cluster of related documents as the resource for bootstrapping.

    pdf6p hongdo_1 12-04-2013 29 4   Download

  • Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In this paper, we exploit semi-supervised learning with the co-training algorithm for automatic detection of coarse level representation of prosodic events such as pitch accents, intonational phrase boundaries, and break indices. ...

    pdf9p hongphan_1 14-04-2013 14 3   Download

  • We investigate the problem of ordering medical events in unstructured clinical narratives by learning to rank them based on their time of occurrence. We represent each medical event as a time duration, with a corresponding start and stop, and learn to rank the starts/stops based on their proximity to the admission date.

    pdf5p nghetay_1 07-04-2013 17 2   Download

  • The goal of our research is to improve event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event extraction architecture that progressively “zooms in” on relevant information. Our extraction model includes a document genre classifier to recognize event narratives, two types of sentence classifiers, and noun phrase classifiers to extract role fillers.

    pdf11p hongdo_1 12-04-2013 25 2   Download

  • Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. ...

    pdf4p hongphan_1 15-04-2013 15 2   Download

  • This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions. ...

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  • In this paper we present how the automatic extraction of events from text can be used to both classify narrative texts according to plot quality and produce advice in an interactive learning environment intended to help students with story writing. We focus on the story rewriting task, in which an exemplar story is read to the students and the students rewrite the story in their own words.

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  • This paper describes a fully automatic twostage machine learning architecture that learns temporal relations between pairs of events. The first stage learns the temporal attributes of single event descriptions, such as tense, grammatical aspect, and aspectual class. These imperfect guesses, combined with other linguistic features, are then used in a second stage to classify the temporal relationship between two events. We present both an analysis of our new features and results on the TimeBank Corpus that is 3% higher than previous work that used perfect human tagged features. ...

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  • Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. In this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence.

    pdf7p bunbo_1 17-04-2013 21 2   Download

  • We present work on linking events and fluents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events, and we extend that work to include fluents as well, presenting a common methodology for linking both events and relations to timestamps within the same sentence.

    pdf9p bunthai_1 06-05-2013 22 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.

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  • Microblogging websites such as Twitter offer a wealth of insight into a population’s current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic (e.g., a politician or event), and Sentiment Analysis extracts each tweet’s attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search.

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  • Temporal reasoners for document understanding typically assume that a document’s creation date is known. Algorithms to ground relative time expressions and order events often rely on this timestamp to assist the learner. Unfortunately, the timestamp is not always known, particularly on the Web.

    pdf9p nghetay_1 07-04-2013 21 1   Download

  • This paper presents a joint model for template filling, where the goal is to automatically specify the fields of target relations such as seminar announcements or corporate acquisition events. The approach models mention detection, unification and field extraction in a flexible, feature-rich model that allows for joint modeling of interdependencies at all levels and across fields.

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  • We describe a novel approach to unsupervised learning of the events that make up a script, along with constraints on their temporal ordering. We collect naturallanguage descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph representation of the script’s temporal structure using a multiple sequence alignment algorithm. The evaluation of our system shows that we outperform two informed baselines.

    pdf10p hongdo_1 12-04-2013 18 1   Download

  • We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE , SUSPECT), convicted( JUDGE , SUSPECT )) whose arguments are filled with participant semantic roles defined over words (J UDGE = {judge, jury, court}, P OLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles.

    pdf9p hongphan_1 14-04-2013 9 1   Download


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