Ana-Maria Popescu

Web-scale information extraction in knowitall: (preliminary results)

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAlls architecture and reports on lessons learned for the design of large-scale information extraction systems.

OPINE: Extracting Product Features and Opinions from Reviews

Consumers have to often wade through a large number of on-line reviews in order to make an informed product choice. We introduce OPINE, an unsupervised, high-precision information extraction system which mines product reviews in order to build a model of product features and their evaluation by reviewers.

Towards developing probabilistic generative models for reasoning with natural language representations

Probabilistic generative models have been applied successfully in a wide range of applications that range from speech recognition and part of speech tagging, to machine translation and information retrieval, but, traditionally, applications such as reasoning have been thought to fall outside the scope of the generative framework for both theoretical and practical reasons. Theoretically, it is difficult to imagine, for example, what a reasonable generative story for first-order logic inference might look like. Practically, even if we can conceive of such a story, it is unclear how one can obtain sufficient amounts of training data. In this paper, we discuss how by embracing a less restrictive notion of inference, one can build generative models of inference that can be trained on massive amounts of naturally occurring texts; and text-based deduction and abduction decoding algorithms.

Board coherence in Pinterest: non-visual aspects of a visual site

Pinterest is a fast-growing interest network with significant user engagement and monetization potential. This paper explores quality signals for Pinterest boards, in particular the notion of board coherence. We find that coherence can be assessed with promising results and we explore its relation to quality signals based on social interaction.