information extraction 2 day 37
Download
Skip this Video
Download Presentation
Information extraction 2 Day 37

Loading in 2 Seconds...

play fullscreen
1 / 21

Information extraction 2 Day 37 - PowerPoint PPT Presentation


  • 86 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Information extraction 2 Day 37' - deon


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
information extraction 2 day 37

Information extraction 2Day 37

LING 681.02

Computational Linguistics

Harry Howard

Tulane University

course organization
Course organization
  • http://www.tulane.edu/~howard/NLP/

LING 681.02, Prof. Howard, Tulane University

workflow for info extraction
Workflow for info extraction

LING 681.02, Prof. Howard, Tulane University

chunking
Chunking

LING 681.02, Prof. Howard, Tulane University

hierarchical structure
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

results
Results

LING 681.02, Prof. Howard, Tulane University

overview
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

nested structure
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

trees
Trees

(S

(NP Alice)

(VP

(V chased)

(NP

(Det the)

(N rabbit))))

LING 681.02, Prof. Howard, Tulane University

trees in nltk
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

tree traversal
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

more named entities
More named entities

LING 681.02, Prof. Howard, Tulane University

overview1
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

relation extraction
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

postscript
Postscript
  • Much of what we have described goes under the heading of text mining.

LING 681.02, Prof. Howard, Tulane University

quiz grades
Quiz grades

LING 681.02, Prof. Howard, Tulane University

next time

Next time

No quiz

NLPP §10

Analyzing the meaning of sentences

ad