1. Disambiguation Read J & M Chapter 17.1 – 17.2

2. The Problem Washington Loses Appeal on Steel Duties Sue caught the bass with the new rod. Sue played the bass with the awesome sound. Sue cooked. The potatoes cooked. I saw a spring flying through the air.

3. Specific Problems Choosing the right meaning for each word. Mapping arguments to thematic roles. Resolving parsing ambiguities.

4. Possible Solutions Integrate the use of semantic knowledge into parsing. Extreme approach: semantic grammars. Build syntactic constituents and pass them to semantics for evaluation. Reject ill formed ones or simply rank order them by likelihood. Build a meaning representation of an entire sentence and attempt to integrate it into the larger context. Pro: can use larger context when local information is not enough Con: explosion in number of possibilities

5. Main Approaches Drive the process with a knowledge base: Selectional restrictions Preference semantics/selectional association Count the words

6. Selectional Restrictions Mapping to Thematic roles: They serve meatloaf on Tuesdays. American serves Dallas and Austin. O’s serves breakfast. Which pubs serve minors? Choosing the right meaning: John serves with a backhand.

7. Selectional Restrictions – Thematic Roles 1.They serve meatloaf on Tuesdays. 2.American serves Dallas and Austin. 3.O’s serves breakfast. 4.Which pubs serve minors? Using FOPC: 1. z y  x Isa(x, serve1)  Agent(x, y)  AE(x, z)  Isa(z, Food) (Note that if meatloaf Isa Food, this will work. 2. z y  x Isa(x, serve2)  Agent(x, y)  AE(x, z)  Isa(z, Location) Or we can skip the full power of FOPC and just search in a hyponym structure such as WordNet.

8. Selectional Restrictions – Polysemy and Homonyms The spring fed the creek.

9. Selectional Restrictions Solve Obvious Problems but Have Limitations I want to eat seafood. I want to eat someplace cheap. I want to eat Italian. What kind of dishes do you like?Restrictions aren’t strong enough John was green with envy.Simple class info not always enough The circus performer swallowed fire.Unusual but true It Was Just As The Trees WhisperedPoetic Washington refused to comment.Metonymy Call me on my cell.Constant changes – robustness

10. Give Up on Knowledge – Just Count Things Example – Word Sense Disambiguation An electric guitar and a bass player stand off to one side.

11. Just Count Things - Input Input: Typically a feature vector that represents co-occurrence or collocation facts. Example: An electric guitar and a bass player stand off to one side. A collocation vector: [guitar, NN1, and, CJC, player, NN1, stand, VVB] A co-occurrence vector: First, look at texts containing the target word and find the n most frequent content words. Use these as the features. So we might use the following: [fishing, big, sound, player, fly, rod, pound, double runs, playing, guitar, band] producing the vector: [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0]

12. Just Count Things – Choosing An algorithm: The Naïve Bayes Classifier We can’t collect enough data to use whole feature vectors, so we assume that the words are independent and break it up: P(s) is the same throughout the vector and P(V) is the same for all candidates, given the vector, so

13. Just Count Things - Training Training the classifier: What do we need? Prior probabilities for each of the word senses. Probabilities for each feature given some particular sense. To get these, we need to start with a sense-tagged corpus. So this is an example of a supervised learning method.

14. Just Count Things in a Dictionary The advantage: Dictionaries already exist for other reasons so if we can use them, we can avoid hand tagging a large corpus. Example (from Lesk): choose the correct meaning for cone in pine cone: pine:1 kinds of evergreen tree with needle-shaped leaves 2 waste away through sorrow or illness cone1 solid body which narrows to a point 2 something of this shape whether solid or hollow 3 fruit of certain evergreen trees We compare the three definitions of cone to the words in the definitions for pine. We choose 3.

15. Limitations of the Dictionary Method Definitions are too short. What if we don’t know which sense to use for the surrounding words? Sometimes this is fixed in dictionaries by the use of subject codes. Dictionaries aren’t always up to date either, although they get updated much more often than they used to. Example: Look at Longman’s: http://www.longman.com/dictionaries/webdictionary.html For cell, instant message

16. Counting Things for the Other Tasks Mapping arguments to thematic roles. Resolving parsing ambiguities. Use the same techniques but we need an appropriate set of features and a training set. Example: http://acl.ldc.upenn.edu/P/P00/P00-1014.pdfhttp://acl.ldc.upenn.edu/P/P00/P00-1014.pdf