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Information extraction 2 Day 37

Information extraction 2 Day 37. LING 681.02 Computational Linguistics Harry Howard Tulane University. Course organization. http://www.tulane.edu/~howard/NLP/. Extracting information from text. NLPP §7. Workflow for info extraction. Chunking. Hierarchical structure.

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Information extraction 2 Day 37

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  1. Information extraction 2Day 37 LING 681.02 Computational Linguistics Harry Howard Tulane University

  2. Course organization • http://www.tulane.edu/~howard/NLP/ LING 681.02, Prof. Howard, Tulane University

  3. Extracting information from text NLPP §7

  4. Workflow for info extraction LING 681.02, Prof. Howard, Tulane University

  5. Chunking LING 681.02, Prof. Howard, Tulane University

  6. Hierarchical structure • Chunks can be represented as trees, seen in the chunk parser from last time. • Hierarchy from tags • IOB tags • Inside, Outside, Begin • IOB tags for example: We PRP B-NP saw VBD O the DT B-NP little JJ I-NP yellow JJ I-NP dog NN I-NP LING 681.02, Prof. Howard, Tulane University

  7. Results LING 681.02, Prof. Howard, Tulane University

  8. Developing & evaluating chunkers NLPP 7.3

  9. Overview • Need a corpus that is already chunked to evaluate a new chunker. • CoNLL-2000 Chunking Corpus from Wall Street Journal • Evaluation • Training LING 681.02, Prof. Howard, Tulane University

  10. Recursion in ling structure NLPP 7.4

  11. Nested structure • We have looked at trees, but they are different from normal linguistic trees. • NP chunks do not contain NP chunks, ie. they are nor recursive. • They do not go arbitrarily deep. • (Example on board.) LING 681.02, Prof. Howard, Tulane University

  12. Trees (S (NP Alice) (VP (V chased) (NP (Det the) (N rabbit)))) LING 681.02, Prof. Howard, Tulane University

  13. Trees in NLTK • A tree is created in NLTK by giving a node label and a list of children: >>> tree1 = nltk.Tree('NP', ['Alice']) >>> print tree1 (NP Alice) >>> tree2 = nltk.Tree('NP', ['the', 'rabbit']) >>> print tree2 (NP the rabbit) • They can be incorporated into successively larger trees as follows: >>> tree3 = nltk.Tree('VP', ['chased', tree2]) >>> tree4 = nltk.Tree('S', [tree1, tree3]) >>> print tree4 (S (NP Alice) (VP chased (NP the rabbit))) LING 681.02, Prof. Howard, Tulane University

  14. Tree traversal def traverse(t): try: t.node except AttributeError: print t, else: # Now we know that t.node is defined print '(', t.node, for child in t: traverse(child) print ')', >>> t = nltk.Tree('(S (NP Alice) (VP chased (NP the rabbit)))') >>> traverse(t) ( S ( NP Alice ) ( VP chased ( NP the rabbit ) ) ) LING 681.02, Prof. Howard, Tulane University

  15. Named entity recognition & relation extraction NLPP 7.5 & 7.6

  16. More named entities LING 681.02, Prof. Howard, Tulane University

  17. Overview • Identify all textual mentions of a named entity (NE): • Identify boundaries of a NE; • Identify its type. • Classifiers are good at this. LING 681.02, Prof. Howard, Tulane University

  18. Relation extraction • Once named entities have been identified in a text, we then want to extract the relations that exist between them. • We will typically look for relations between specified types of a named entity. • One way of approaching this task is to initially look for all triples of the form (X, α, Y), where X and Y are named entities of the required types, and α is the string of words that intervenes between X and Y. • We can then use regular expressions to pull out just those instances of α that express the relation that we are looking for. LING 681.02, Prof. Howard, Tulane University

  19. Postscript • Much of what we have described goes under the heading of text mining. LING 681.02, Prof. Howard, Tulane University

  20. Quiz grades LING 681.02, Prof. Howard, Tulane University

  21. Next time No quiz NLPP §10 Analyzing the meaning of sentences

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