# Presentation: 035533

## Ontolex: A Cognitive Model of Language Learning

- by Emily Fortuna
- NIST Information Technology Laboratory
- Rice University

## Comprehensive searches for webpages

- one-to-one method

- Thesis topic:

- You want all related topics:
-  Recipes
-  Sam
-  Dr. Seuss
-  articles in other languages

**Notes:**
It is not enough to assume that a page containing one of list items will also contain green eggs and ham. 
The history and function of... through the ages... 

## Comprehensive searches for webpages

- green eggs and ham

- huevos verdes con jamón

- le uova verde e prosciutto

- oeufs et jambon verts

- grüne Eier und Schinken

- Thesis topic:

- one-to-one method

- Dr. Seuss

- Sam

- Recipes

- Exponential Growth

**Notes:**
**exponential growth

## Create a Web of Related Concepts

- Authors

**Notes:**
SEMANTIC WEB

## Comprehensive searches for webpages

- huevos verdes con jamón

- Virent Ova! Viret Perna!!

- le uova verde e prosciutto

- 绿色蛋和火腿

- зеленые яичка и ветчина

- oeufs et jambon verts

- 녹색 계란 및 햄

- πράσινα αυγά και ζαμπόν

- grüne Eier und Schinken

- اخضر البيض ولحم الخنزير

- A better design: Ontologies

- Thesis topic:

-  Zielone jajka i szynka

## » » » Ontology? « « «

- A representation of how concepts in a domain relate to one another.
- In the form of
- A simple hierarchy
- A more complex web (can be recursive)

## » » » Ontology  Hierarchy « « «

- Scarecrow

*Slots* give additional information
- 	For example, “fur color” for Lion, Toto, and the flying 			monkey.

- flying monkey

## Ontologies help make Computers more intelligent

- Drawing conclusions
- Example:
- “Glinda cast a spell.”
- “Dorthy cast a spell.”
- Also:
- “Dorthy’s ruby slippers”
- “Toto’s ruby slippers”

**Notes:**
AMBIGUITY of natural language

## » » » Ontology  Web « « «

- Source: Sowa, J. F. *Knowledge Representation: Logical, Philosophical, and Computational Foundations*. Pacific Grove,	CA: Brooks Cole Publishing Co. http://www.jfsowa.com/ontology/index.htm, 2000.

## » » » Goals of Ontolex « « «

- Create a second language acquisition computer model that is
- Easily understandable
-  Python programming language
- Available
-  Internet
- Reusable
-  Ontologies and software design
-  Any human language

## » » » Ideas powering Ontolex « « «

- Ontologies
- Model *for* a model
- Sentence generation technique

**Notes:**
1 – ontologies – syntax plus semantics
2 – not a translation program – cognitive model

## Powering Ontolex  One Unified Ontology

**Notes:**
At a High level
Etc.
**concepts -- []

## » » » Not just Nouns « « «

- All parts of speech can potentially derive from the concepts in Ontolex’s ontology

- Concept:

- Words derived:

## Ontolex’s three-tier System

- [invent]

- Concept Layer
- (language independent)

**Notes:**
define lexeme

## Three-tier system: Another perspective

- = The Ontology

- = Lexeme Language A

- = Lexeme Language B

- = Word Language B

- = Word Language A

## » » » Ideas powering Ontolex « « «

- Ontologies
- Model *for* a model
- Sentence generation technique

## » » » Model design « « «

- 3 sections:
- Vocabulary building
- Form Sentences and Translation
- Unique Sentence Generation

**Notes:**
Give a high level discussion of these parts for this sectoin. – talk as much as you want about the first two, just not the third.
*****NOT a translataion tool!!!

## » » » Vocabulary Building « « «

- “Silent phase” of language learning
- Ontology Builder
- Sentence Learner
- Database Viewer

## » » » Ontology Building – Tree « « «

## » » » Sentence Learner « « «

**Notes:**
Links words to concepts, 
Saves sentence structures
Saves information about each word
Allows for idioms – NOT WORD FOR WORD TRANSLATION

## Form Sentences and Translation

- Learner translates to native language to understand foreign language
- Learner uses sentence structures he/she has heard before
- Under development

## Difficulty with Parse Trees

- Problem: null subject languages, like Spanish and Japanese
- Nothing to put in NP
- Programming for individual transformations

## » » » Unique Sentence Generation « « «

- Specifying Semantics
- Proposed model:
- Concepts (ontology)  Sentences
- User selects
- Verb
- Nouns (Subject, Object, etc.)
- Language for generated sentence

**Notes:**
Define: semantics – the meaning of words as opposed to syntax
Proposing a way sentences are generated. Ideas  sentences, computer is doing a way we think humans do it

## » » » Semantics Example « « «

- Example:
- Verb: [eat]
- Nouns: [human], [bread]
- Language: Spanish

## » » » Sentence Generation Process « « «

- Get Overall Sentence Structure
- Randomly select substructures
- Add lexemes
- Convert to words
- Receive user feedback

## » » » Sentence Generation « « «

- Get Overall Sentence Structure
- Any combination of the any number of the following:
- Subject
- Verb Phrase
- Object
- Indirect Object
- Example: [eat] [human] [bread]  O VP S
- Order acceptable for Spanish: S VP O

**Notes:**
legal utterance

## 2. Randomly select substructures

## 3. Create language tree with lexemes

- “man”

- “eat”

- “bread”

## 4. Create language tree with words

- “man”

- “eat”

- “bread”

- “The fat man eats bread.”

**Notes:**
**Results in UNIQUE sentences generated each time you submit those items!!!!
** Random – allows new combinations – learning	
** tries superclasses

## 5. Receive User Feedback

*Learns* new syntactic and semantic combinations
- Uses reinforcement

## » » » Implications « « «

- Short term:
- Model
- Test language acquisition theories with computers
- Long term:
- Ontologies  more intelligent computers
- “think” and “learn” like humans
- Voice recognition
- Computer systems can interact with each other
- Better human-computer communication

**Notes:**
Future advancements – not a satisfactory model -- phonetics
Computational science approach – give tools to try out theories and test and come up with new things.
time flies

## » » » Acknowledgements « « «

- Thanks to:
- Dr. Larry Reeker, my advisor
- NIST Information Technology Laboratory for funding this research and participating in the SURF program
- Rice University for informing me about this summer opportunity
- Dan Sandler, Michael Benza, Christopher Warrington, and the Python Tutor mailing list for programming language implementation discussion and suggestions in the early stages of this project.

## http://ontolex.nist.gov

## More Information

- In case you can’t get enough

## » » » Website Layout « « «

## Model-View-Controller framework

## Database Viewer

## Form Sentences and Translation