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Dayne Freitag

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers
2 author rows

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9

AAAI Conference 2025 Conference Paper

CROSSNEWS: A Cross-Genre Authorship Verification and Attribution Benchmark

  • Marcus Ma
  • Duong Minh Le
  • Junmo Kang
  • Yao Dou
  • John Cadigan
  • Dayne Freitag
  • Alan Ritter
  • Wei Xu

Authorship models have historically generalized poorly to new domains because of the wide distribution of author-identifying signals across domains. In particular, the effects of topic and genre are highly domain-dependent and impact authorship analysis performance greatly. This paper addresses the existing data gap in authorship for these resources by introducing CROSSNEWS, a novel cross-genre dataset that connects formal journalistic articles and casual social media posts. CROSSNEWS is the largest authorship dataset of its kind for supporting both verification and attribution tasks, with comprehensive topic and genre annotations. We use CROSSNEWS to demonstrate that current models exhibit poor performance in genre transfer scenarios, underscoring the need for authorship models robust to genre-specific effects. We also explore SELMA, a new LLM embedding approach for large-scale authorship setups that outperforms existing models in both same-genre and cross-genre settings.

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AAAI Conference 2000 Conference Paper

Boosted Wrapper Induction

  • Dayne Freitag

Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures (“wrappers”) for highly structured text such as Web pages produced by CGI scripts. For suitably regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. Boosting is a technique for improving the performance of a simple machine learning algorithm by repeatedly applying it to the training set with different example weightings. We describe an algorithm that learns simple, low-coverage wrapper-like extraction patterns, which we then apply to conventional information extraction problems using boosting. The result is BWI, a trainable information extraction system with a strong precision bias and F1 performance better than state-of-the-art techniques in many domains.

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AIJ Journal 2000 Journal Article

Learning to construct knowledge bases from the World Wide Web

  • Mark Craven
  • Dan DiPasquo
  • Dayne Freitag
  • Andrew McCallum
  • Tom Mitchell
  • Kamal Nigam
  • Seán Slattery

The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs. The first is an ontology that defines the classes (e. g. , company, person, employee, product) and relations (e. g. , employed_by, produced_by) of interest when creating the knowledge base. The second is a set of training data consisting of labeled regions of hypertext that represent instances of these classes and relations. Given these inputs, the system learns to extract information from other pages and hyperlinks on the Web. This article describes our general approach, several machine learning algorithms for this task, and promising initial results with a prototype system that has created a knowledge base describing university people, courses, and research projects.

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AAAI Conference 1998 Conference Paper

Learning to Extract Symbolic Knowledge from the World Wide Web

  • Mark Craven
  • Dayne Freitag
  • Tom Mitchell

The World Wide Webis a vast source of information accessible to computers, but understandable only to humans. Thegoal of the research described here is to automatically create a computer understandable world wide knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Webinformation, and promote newuses of the Webto support knowledgebased inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs: an ontology defining the classes and relations of interest, and a set of training data consisting of labeled reg-ions of hypertext representing instances of these classes and relations. Giventhese inputs, the system learns to extract information from other pages and hyperlinks on the Web. This paper describes our general approach, several machinelearning algorithms for this task, and promising initial results with a prototype system.

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