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lec5 - CS 6093 Lecture 5 Basic Information Extraction Cong...

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CS 6093 Lecture 5 Basic Information Extraction Cong Yu
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Groups and Projects Primarily supervised by Fernando P03: Detecting Trends in Facebook/Twitter Feeds Maggie, Nitin, Quentin P12: Learning to Rank with Label Noise • Josh Primarily supervised by Cong P10: Mining Patterns from Status Updates Devansh, Prayag, Ting P11: Recommendations in Social Networks Konstantinos, Rata, Yigit Advice: do the following as soon as possible Survey the literature Settle on a well defined problem to attack Identify appropriate data set to use for experiments
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Midterm Report 30% of the overall grade Due: 5p ET March 21 st Deadline is strictly enforced Late submission policy One day delay (5p ET March 22 nd ): 5% reduction of score Two day delay (5p ET March 23 rd ): 20% reduction of score Three day delay (5p ET March 24 th ): 50% reduction of score No longer accepted after 5p ET March 24 th . How: email your report to both instructors Format: 6 pages maximum, including figures, citations, and appendixes 12pt Time New Roman, single column Word or PDF only
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Writing Guideline (Suggested) Abstract A clear and concise description of the project Introduction and Motivation Describe importance and applications of the problem Provide motivating examples (real examples are much better) Related Work Describe existing work in the field Both research papers and industrial systems Summarize the solutions of important references Formal Problem Definition and Solution Overview Formally define the problem being solved Provide an overview of proposed solution
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Writing Guideline (Suggested) System Architecture Provide a system architecture Describe components already implemented Describe components to be implemented Identify off-the-shelf components in the system Preliminary Study Datasets downloaded and insights obtained from the dataset APIs examined and experience working with the APIs (i.e., what’s doable and what’s not) If some preliminary experiments are performed, describe them Bibliography List of citations
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Any Questions?
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Today’s Outline Overview of Information Extraction What, why, and history IE pipeline & tasks Basic Information Extraction Techniques Named entity recognition Wrapper technology Scaling up Information Extraction Pattern based Iterative extraction Cost based extraction plan optimization
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What is Information Extraction? structured record extraction concept recognition
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What is Information Extraction ID Person Position Place 1 Torsten Suel Professor NYU-Poly 2 Jan Pedersen Chief Scientist Bing Search Concept Recognition Detecting the occurrences of certain concepts (people, organization, date, etc.) within a document Structure Record Extraction
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Why Information Extraction?
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