Gr based improved parse selection

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Lexical models to improve parse selection

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Gr based improved parse selection

  1. 1. GR-Based Improved Parse Selection<br />Sriwantha Sri Aravinda Attanayake<br />University of Cambridge<br />
  2. 2. Grammar Relations (GR)<br />Indirect Object<br />John saw a cat with a telescope<br />Subject<br />Determiner <br />
  3. 3. Grammar Relations (GR)<br />John saw a cat with a telescope<br /><GR List><br />(ncsubj saw John )<br />(iobj saw with )<br />(dobj saw cat )<br />(dobj with telescope )<br />(det telescope a )<br />(det cat a )<br />
  4. 4. Luckily… <br />Parser<br /><GR List><br />(ncsubj saw John )<br />(iobj saw with )<br />(dobj saw cat )<br />(dobj with telescope )<br />(det telescope a )<br />(det cat a )<br />
  5. 5. Re Ranking<br />Choice 1<br />(|ncsubj| |saw| |John| _)<br />(|iobj| |saw| |with|)<br />(|dobj| |saw| |cat|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| |a|)<br />(|det| |cat| |a|)<br />Choice 2<br />(|ncsubj| |saw| |John| _)<br />(|dobj| |saw| |cat|)<br />(|det| |cat| |a|)<br />(|ncmod| _ |cat| |with|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| )<br />Choice 3<br />(|ncsubj| |saw| |John| _)<br />(|ncmod| _ |saw| |with|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| |a|)<br />(|dobj| |saw| |cat|)<br />
  6. 6. John saw acatwith a telescope<br />a cat a + cat<br />NP DT+ NN<br />
  7. 7. John saw a cat with a telescope<br />Saw + with : 80%<br />cat + with : 40%<br />
  8. 8. Parse1<br />(|ncsubj| |saw| |John| _)<br />(|iobj| |saw| |with|)<br />(|dobj| |saw| |cat|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| |a|)<br />(|det| |cat| |a|)<br />Parse 2<br />(|ncsubj| |saw| |John| _)<br />(|dobj| |saw| |cat|)<br />(|det| |cat| |a|)<br />(|ncmod| |cat| |with|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| |a| )<br />Parse 3<br />(|ncsubj| |saw| |John| _)<br />(|ncmod| _ |saw| |with|)<br />(|dobj| |with| |telescope|)<br />(|det| |telescope| |a|)<br />(|dobj| |saw| |cat|)<br />Count=1000<br />Count=5000<br />2000<br />100 million wordBritish National Corpus<br />(BNC)<br />
  9. 9. F-Score<br />(ncsubj saw John )<br />100% accurate, <br />Poor recall<br />(ncsubj saw John )<br />(iobj saw with )<br />(dobj saw cat )<br />(ncsubj saw John )<br />(iobj saw with )<br />(dobj saw cat )<br />(dobj with telescope)<br />(det telescope a )<br />(det cat a )<br />100% recall, <br />Poor accuracy<br />
  10. 10. Current Progress<br />F Score Improvement 1%<br />MRR (10 Parses) about 6%<br />
  11. 11. Challenges<br />(dobj saw cat ) Count=0<br />(dobj saw dog) Count=0<br />(dobj saw it) Count=3<br />(dobj saw {any word}) Count=45<br />(dobj of and) Count=170,203<br />Score= Average (Count) <br />
  12. 12.
  13. 13. End<br />Production of ssauba2@cam.ac.uk<br />

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