Information Extraction Referatsthemen

1. Information ExtractionReferatsthemen CIS, LMU München Winter Semester 2013-2014 Dr. Alexander Fraser, CIS

2. Information Extraction – Reminder • Vorlesung • Learn the basics of Information Extraction (IE) • Klausur – only on the Vorlesung! • Seminar • Deeper understanding of IE topics • Each student who wants a Schein will have to make a presentation on IE • 25 minutes (powerpoint, LaTeX, Mac) • If two students work together, 40 minutes (each student speaks for 20 minutes) • Hausarbeit • 6 pages worked out, due 3 weeks after the Referat • Separate for each student!

3. Topics • Topic will be presented in roughly the same order as the related topics are discussed in the Vorlesung • Most of the topics require you to do a literature search • There will usually be one article (or maybe two) which you find is the key source • If appropriate, please turn in PDF files of the key article and a few other important articles • There are a few projects involving programming • These are particularly suitable to be done by two students

4. Referat • 25 minutes for one student • 40 minutes for two • Start with what the problem is, and why it is interesting to solve it (motivation!) • It is often useful to present an example and refer to it several times • Then go into the details • If appropriate for your topic, do an analysis • Don't forget to address the disadvantages of the approach as well as the advantages (advantages tend to be what the authors focus on) • List references and recommend further reading • Have a conclusion slide!

5. Information Extraction and beyond Information Extraction (IE) is the process of extracting structured information from unstructured machine-readable documents Ontological Information Extraction Fact Extraction Instance Extraction Named Entity Recognition ✓ ✓ Tokenization& Normalization Source Selection ...married Elvis on 1967-05-01 05/01/67  1967-05-01 ? Slide from Suchanek

6. History of IE • TOPIC: IE at the Message Understanding Conferences (MUC) • These conferences focused on tasks like extracting merger events from unstructured text • Discuss problems solved, motivations and techniques • Survey the literature

7. Source Selection • TOPIC: Focused web crawling • Why use focused web crawling? • How do focused web crawlers work? • What are the benefits and disadvantages of focused web crawling? • Python: scrapy • Perl: WWW::Mechanize

8. Source Selection • TOPIC: Language Identification • Compare the approaches used • Such as the compression, dictionary and character histogram approaches • Discuss the issue of 8-bit encodings and dealing with code pages

9. Source Selection • TOPIC: Wrappers • Wrappers are used to extract tuples (database entries) from structured web sites • Discuss the different ways to create wrappers • Advantages and disadvantages • How do wrappers deal with changing websites? • Give some examples of different wrapper creation software packages and discuss their pros and cons

10. Rule-based Named Entity Recognition: Regular Sets • TOPIC: Regular Sets in the Proceedings of the European Parliament (Europarl corpus) • Annotate regular classes in the Europarl corpora in both English and German • Interesting regular classes: report-IDs and dates. Others could include nationalities/countries or monetary amounts. • Programming intensive: this will involve writing a lot of regular expressions, and analysis

11. Named Entity Recognition – Entity Classes • TOPIC: fine-grained open classes of named entities • Survey the proposed schemes of fine-grained open classes, such as BBN's classes used for question answering • Discuss the advantages and disadvantages of the schemes • Discuss also the difficulty of human annotation – can humans annotate these classes reliably?

12. Named Entity Recognition – Training Data • TOPIC: Crowd-sourcing with Amazon Mechanical Turk (AMT) • AMT's motto: artificial artificial intelligence • Using human annotators to get quick (but low quality) annotations • What are the pros and cons of this approach? How well do NER systems perform when trained on this data?

13. Rule-BasedExtraction • TOPIC: Using Local Grammars for Citation Parsing • Discuss how to define regular expressions for the different fields in a citation • Discuss how these regular expressions are combined in the local grammar approach • Present the advantages and disadvantages of this approach to citation parsing

14. Learning Rules for Named Entity Recognition • TOPIC: Learning Rules for Named Entity Recognition • Discuss how rules can be learned given annotated corpora • Present the basic algorithms • Discuss the advantages and disadvantages of these approaches

15. Named Entity Recognition – Statistical Model • TOPIC: Hidden Markov Models for Named Entity Recognition • Discuss how to formulate named entity recognition in the HMM framework • Discuss the transition model and the emission model – which features are useful? • One possible emphasis: what we want the models to be able to do, what problems they have versus other statistical approaches

16. Named Entity Recognition – Statistical Model • TOPIC: Structured Perceptron for Named Entity Recognition • Discuss how to formulate named entity recognition in the structured perceptron framework • This requires previous exposure to machine learning! • What does the structured perceptron framework allow you to do versus the HMM? • Presentation of important features

17. Named Entity Recognition - Supervision • TOPIC: Lightly Supervised Named Entity Recognition • Starting from a few examples ("seed examples"), how do you automatically build a named entity classifier? • This is sometimes referred to as "bootstrapping" • What the problems with this approach – how do you block the process from generalizing too much? • Analyze the pros and cons of this approach

18. Named Entity Recognition - Supervision • TOPIC: Distant supervision for NER • Related to the bootstrapping idea – but here we are using information annotated for a different purpose • How can distant supervision solve the knowledge bottleneck for NER? • What are the advantages and disadvantages of this approach?

19. NER – Toolkit • TOPIC: Stanford NER Toolkit applied to Europarl • Apply the Stanford NER Toolkit to the Europarl corpus, and compare the output on English and German • How does the model work? • What are the differences between the English and German annotations of parallel sentences, where do the models fail?

20. NER – Domain Adaptation • TOPIC: Domain adaptation and failure to adapt • What is the problem of domain adaptation? • How is it addressed in statistical classification approaches to NER? • How well does it work?

21. NER – Twitter • TOPIC: Named Entity Recognition of Entities in Twitter • There has recently been a lot of interest in annotating Twitter • Which set of classes is annotated? What is used as supervised training material, how is it adapted from non-Twitter training sets? • What are the peculiarities of working on 140 character tweets rather than longer articles?

22. NER – BIO Domain • TOPIC: Named Entity Recognition of Biological Entities • Present a specific named entity recognition problem from the biology domain • Which set of classes is annotated? What is used as supervised training material? • What are the difficulties of this domain vs. problems like extraction of company mergers which have been studied longer?

23. Instance Extraction • TOPIC: Applying the Stanford Coreference Pipeline toEuroparl • Apply the StanfordCoreference Pipeline to English Europarl data • How does it work? • What entities does it annotate well, and less well? • Can this information be used to translate English "it" to German?

24. IE for multilingual applications • TOPIC: Transliteration Mining • Transliteration mining is the mining of names which are transliterated from one language to another from a list of word pairs • Present the task of transliteration mining as done in the NEWS conferences (here is the 2010 URL) • http://translit.i2r.a-star.edu.sg/news2010/ • What approaches work well for transliteration mining? • (Some basic statistical modelling background will be necessary here)

25. IE for multilingual applications • TOPIC: Bilingual Terminology Mining • The problem of bilingual terminology mining from comparable corpora is the task of finding terms which are translations of each other given their context • How is this done using the vector space model vs. the pattern-based approach? • Some familiarity with basic information retrieval will be helpful here • What the critical sources of knowledge for this approach?

26. Choosing a topic • Any questions? • I will put these slides on the seminar page later today • Please email me with your choice of topic • I'd also be open to using the Wiki, but I don't see how to make that work • Check the seminar page first to see if it is already taken!