Overview:

Details:

An example of a Focused Textual Entailment domain is that of Document Anonymization. In this domain, guidelines specify a set of relations of interest (these are almost certainly in an abstract form); the task is to identify text in a corresponding set of documents that are entailed by these guidelines. One way to model this problem is as relation detection, where relation participants may be underspecified; for example, a guideline might specify:

"Any civilian charged with a violent crime."

where the domain is "reports of illegal activity".

For the above example, and given a corpus of documents representing reports (police reports, newspaper reports) we would like to scan the documents and detect such cases as:

"Bill Jones, headmaster of the Fisher School in Smalltown, Va., served six months in prison for striking pupil John Smith."

and reject such cases as:

"Bill Jones, Smallville County Clerk, was charged with illegally striking voters from the electoral roll."

We consider this to be an entailment task because shallow methods are unlikely to reliably detect cases where the relevant information needs to be pieced together, either from multiple text segments or via background knowledge.

We consider it to be a focused entailment task because there are a limited number of such guidelines, and the restricted domain permits development of specialized resources that could be expected to significantly enhance performance on this task (in this example, background knowledge of the judicial process, and of the distinction between civilian and non-civilian job titles, seem essential to good performance), which would have significantly less impact in a general Textual Entailment setting.

We are addressing Focused Textual Entailment by developing our Entailment System framework to easily accommodate new, specialized operators that either modify the original text (for example, by identifying instances of job titles or violent crimes) or incorporate background knowledge for use in resolving unification (for example, the inference rule that maps "serve time in jail for X crime" to "charged with X crime"), including a framework for evaluating such modules.

One of the long-term goals of this work is to develop a system that can be adapted by an end user to a particular domain, via an interactive interface that would allow them to identify missing resources and to enhance or correct existing resources. To this end, we are also enhancing the reports our system creates that explain the final entailment decision, with a view to allowing an end user to use this output to correct mistakes or add new information.

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