Political Science: KEDS—A Program for the Machine Coding of Event Data

Schrodt, Philip A.; Davis, Shannon; Weddle, Judith L.

doi:10.1177/089443939401200408

Cited by 98 publications

(44 citation statements)

References 6 publications

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“…Although it is independent of the data format, we will focus on a particular coding scheme to make the exposition more concrete. The Kansas Event Data System (KEDS) [15] is a software tool that automatically extracts events from text. In Sect.…”

Section: Event Datamentioning

confidence: 99%

Summarizing Dynamic Bipolar Conflict Structures

Brandes

Fleischer

Lerner

2006

IEEE Trans. Visual. Comput. Graphics

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We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time-stamped events, and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events, and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict.Keywords: information visualization, text mining, event analysis, time-dependent visualization A preliminary version of this paper has been published in [5].

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Section: Event Datamentioning

confidence: 99%

Summarizing Dynamic Bipolar Conflict Structures

Brandes

Fleischer

Lerner

2006

IEEE Trans. Visual. Comput. Graphics

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“…Early automation of event extraction relied on rule-based pattern matching, using hand-written templates (Chinchor et al, 1993;Schrodt et al, 1994). Modern efforts have focused on supervised machine learning, using annotated corpora for training data, again with pre-defined event types and roles (Miyao et al, 2008;Bjorne and Salakoski, 2011;Bunescu and Mooney, 2004).…”

Section: Related Workmentioning

confidence: 99%

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

Ahn¹

2017

Proceedings of the Events and Stories in the News Workshop

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With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words' functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.

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“…Dictionary or keyword based approaches take an axiomatic approach to classification. The researcher designates that a specific keyword or combination of keywords implies that an event is of a particular class (Schrodt et al, 1994). Thus, there is never any question about whether an event has been correctly classified.…”

Section: Introductionmentioning

confidence: 99%

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

Collingwood

Wilkerson

2012

Journal of Information Technology & Politics

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Text is becoming a central source of data for social science research. With advances in digitization and open records practices, the central challenge has in large part shifted away from availability to usability. Automated text classification methodologies are becoming increasingly important within political science because they hold the promise of substantially reducing the costs of converting text to data for a variety of tasks. In this paper, we consider a number of questions of interest to prospective users of supervised learning methods, which are appropriate to classification tasks where known categories are applied. For the right task, supervised learning methods can dramatically lower the costs associated with labeling large volumes of textual data while maintaining high reliability and accuracy. Information science researchers devote considerable attention to comparing the performance of supervised learning algorithms and different feature representations, but the questions posed are often less directly relevant to the practical concerns of social science researchers. The first question prospective social science users are likely to ask is -how well do such methods work? The second is likely to be -how much do they cost in terms of human labeling effort? Relatedly, how much do marginal improvements in performance cost? We address these questions in the context of a particular dataset -the Congressional Bills Project -which includes more than 400,000 labeled bill titles (19 policy topics). This corpus also provides opportunities to experiment with varying sample sizes and sampling methodologies. We are ultimately able to locate an accuracy/efficiency sweet spot of sorts for this dataset by leveraging results generated by an ensemble of supervised learning algorithms.

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Political Science: KEDS—A Program for the Machine Coding of Event Data

Cited by 98 publications

References 6 publications

Summarizing Dynamic Bipolar Conflict Structures

Summarizing Dynamic Bipolar Conflict Structures

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

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