1. Natural Language Query Interface Mostafa Karkache & Bryce Wenninger

2. Outline Natural Language Query Interface Introduction What is Natural Language Query Interface? Why do we need this type interface? Problems implementing this interface. Ambiguity i.e. Semantics Size of the language Syntax And Grammars Anaphora Indexicality Metaphor NL domains of application Internet Information retrieval : search engines Information filtering: document grouping Database Conclusion: Current status Future trend

3. Introduction What is Natural Query Language Interface? What is it for? Where would it be used? Why do we need this interface? How much would it really help? Is it even possible?

4. Problems with implementation Ambiguity i.e. Semantics Size of the language Syntax And Grammars Anaphora Indexicality Metaphor

5. Problems with implementation… Ambiguity and Semantics The boy saw the man on the hill with the telescope

6. Ambiguity and Semantics What is wrong with this sentence? Ans: Too ambiguous How many different meanings can it have? The only way to truly understand is to be there.

7. Ambiguity and Semantics Another example of problems is with semantics. The word up can have many meanings when used in different ways such as, “Look up there”, “It is up to me”, “Is he up to the task?”, “She is not up yet”, “Starting up”, “What’s up dude?”

8. More problems with implementation… Size of the language Most Natural Languages have enormous vocabulary. Example: The English language has approximately 3 Million words, and counting. 200,000 of which are in common use today (and this isn’t counting semantics).

9. More problems with implementation… Syntax And Grammars Languages have alphabets and rules Sample alphabet {a, b} Sample (rewrite) rules: S  aSb S  ba This will generate words of type ba, abab, aababb, aaababbb

10. Syntax And Grammars… English’s main constituents: Sentences Noun phrases Verb phrases Prepositional phrases

11. A sample English grammar S -> NP VP NP -> Det NOMINAL NOMINAL -> Noun VP -> Verb Det -> a Noun -> table Verb -> found

12. Any Problem with that grammar? It is context free grammar, it only account for the syntactic structure. CFG works fine for any high level language. How about the semantics of the words?

13. Semantic Representations Can we create representations of the meanings of the English words?. This is not an easy task. It is a very complex task. A context sensitive grammar is needed.

14. More problems with implementation… Anaphora What is Anaphora? Pronouns and Nouns Why is it a problem? Key words have to be tallied. How would it have to be Handled?

15. More problems with implementation… Indexicality A sentence that refers to a situation (place or time) Example: “I am over here” Where is “here”? Who is “I”?

16. More problems with implementation… Metaphor Non literal use of a word. “This process was killed because it ran out of resources” Meaning in manufacturing vs. computers.

17. Domains of Application Internet Information retrieval: with search engines Information filtering: document grouping More... Database

18. NLIQ and the internet Information retrieval : an English query is issued to a search engine, Documents relevant to the query are returned.

19. Two methods are used Exact matching Inexact matching

20. Exact Matching restrictive and is known for low hits

21. Inexact Matching higher hit rate …But User might have to scan a lot of returned documents!!

22. How does it work? select the ‘candidate key’ words from the query ‘a’, ‘the’, ‘an’ etc would not make it Count the key words in the documents “running”, “run”, “ran” and “runner” would count as one

23. How does it work… Rank the documents by frequency of key words found

24. Information filtering Documents are first prepared and then searched Documents are ranked by topics

25. NLQI and Database Database business, an industry that runs in the billions of dollars a more user-friendly interface between the user and the machine is needed NLQI seems to fit well

26. Example of DB use Give me the names of the employees of Banks Of America who signed up for 401k? Sounds easy? To humans, yes Not to machines!!

27. What does “up” mean? Give me the names of the employees of Banks Of America who signed up for 401k?

28. Solution! Create an index that has all the meanings of every word that can be used in database domain!!! Then guess what “up” would mean

29. NLQI and Database… Can’t use NLQI to create a database: ---Data integrity compromised Could use NLQI for information retrieval: ---Performance compromised, to say the least

30. Current status NLQI is used in many areas today, but it is very (very) application specific. This is to avoid a lot of the problems discussed in this presentation. Broad use and what NLQI is truly capable of has not yet been realized.

31. Future trend Where is it going? The trend is to store more and more data per user to help determine exactly what semantics the user is really intending. This is called incremental enhancement of the data retrieval process Will it ever get there?

32. Questions? You’ve got questions, we’ve got answers (hopefully).