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Viewing 5 videos from 2014 in Talks by Visiting Speakers See AI2’s full collection of videos on our YouTube channel.
    • November 10, 2014

      Alan Akbik

      The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this talk, I present an approach and a graphical tool that allows even novice users to quickly and easily define extraction patterns over dependency trees and directly execute them on a very large text corpus. This enables users to explore a corpus for structured information of interest in a highly interactive and data-guided fashion, and allows them to create extractors for those semantic relations they find interesting. I then present a project in which we use Information Extraction to automatically construct a very large common sense knowledge base. This knowledge base - dubbed "The Weltmodell" - contains common sense facts that pertain to proper noun concepts; an example of this is the concept "coffee", for which we know that it is typically drunk by a person or brought by a waiter. I show how we mine such information from very large amounts of text, how we quantify notions such as typicality and similarity, and discuss some ideas how such world knowledge can be used to address reasoning tasks.

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    • October 1, 2014

      Chris Callison-Burch

      I will present my method for learning paraphrases - pairs of English expressions with equivalent meaning - from bilingual parallel corpora, which are more commonly used to train statistical machine translation systems. My method equates pairs of English phrases like --thrown into jail, imprisoned-- when they share an aligned foreign phrase like festgenommen. Because bitexts are large and because a phrase can be aligned many different foreign phrases including phrases in multiple foreign languages, the method extracts a diverse set of paraphrases. For thrown into jail, we not only learn imprisoned, but also arrested, detained, incarcerated, jailed, locked up, taken into custody, and thrown into prison, along with a set of incorrect/noisy paraphrases. I'll show a number of methods for filtering out the poor paraphrases, by defining a paraphrase probability calculated from translation model probabilities, and by re-ranking the candidate paraphrases using monolingual distributional similarity measures.

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    • August 5, 2014

      Jonathan Berant

      Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this talk, I will focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.

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    • February 26, 2014

      Dafna Shahaf

      The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally: The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising.

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    • February 26, 2014

      Brendan O'Connor

      What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? Less

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