DYNAMIC BUILDING OF DOMAIN SPECIFIC LEXICONS USING EMERGENT SEMANTICSFinal Presentation

Matt Selway 100079967Supervisor: Professor Markus Stumptner

Knowledge and Software Engineering LaboratorySchool of Computer and Information Science

CONTENTS

Motivations and Goals Research Questions Method Experiments and Results Summary and Conclusions Limitations and Future Work

MOTIVATIONS AND GOALS

Kleiner et al. (2009) developed a very different approach to Natural Language Processing (NLP) Treat NLP as Model Transformation problem Utilise Configuration as a model transformation

Model transformation is process of taking input models and creating output models from them Foundation of Model Driven Engineering

Configuration is a constraint based searching technique In this case the constraints are conformance to the

desired meta model

MOTIVATIONS AND GOALS

Overview of Process (Kleiner et al. 2009)

Method shows promising results However, requires use of predefined lexicon

MOTIVATIONS AND GOALS

Issues for practical applications:1. Can take a long time to manually build a

complete lexicon, even for a Specific Domain

2. Predefined lexicon is static3. Reduces level of automation

MOTIVATIONS AND GOALS

Short-range Goals:1. At least partially automated creation of domain specific

lexicons directly from the input text and external resources to retrieve lexical data

2. Make updates a natural part of the system

3. Allow sharing/reuse of lexical information

Long-range Goals:4. Improve the automated analysis of specifications

5. Support research into semantic interoperability

6. Develop global agreement on lexicons/ontologies

RESEARCH QUESTIONS

Can we reduce or eliminate the need to manually predefine a lexicon by dynamically building a lexicon based on the input text?

How much of a reduction can be gained?

How well does it work? (i.e. accuracy of retrieved data, how much data is automatically retrieved)

What are its limitations?

METHOD Developed an experimental system

Attempted to use emergent semantics and semiotic dynamics in a similar way to that described by Steels and Hanappe (2006) for the interoperability of collective information systems.

They propose a multi-agent system that uses communication to arrive at an agreement on the meaning of the data, its tags, and its categories.

They take advantage of the semiotic triad between data, tags, and categories in user taxonomies (e.g. Bookmarks in a web browser) Semiotic triad implies a meaningful relationship between its

three components

METHOD

Basic semiotic triad (Steels & Hanappe, 2006)

Similarly there exists a semiotic triad between a word, its use, and the domain it is used in.

Idea is that this triad can be used in dynamically developing domain specific lexicons between information agents.

METHOD (DESIGN)

Multi-agent System Lexical information retrieved from other

agents Initial data downloaded from online sources User feedback adjusts the retrieved data Agents update their lexicons and

associations to lexicons based on user feedback (using semiotic relationship) Lots of changes indicates the agents are actually

using different domains Few changes indicates updates to the lexicon in

the same domain

METHOD (ONLINE SOURCES)

Surveyed online lexicons/ontologies (CYC, WordNet, EDR) and dictionaries (Oxford, ‘The Free Dictionary’, ‘Your Dictionary’)

Excluded CYC, WordNet, EDR as not suitable Turned to standard online dictionaries

Official dictionaries Oxford/Harvard not suitable (want money for access)

Discovered the ‘The Free Dictionary’ Large number of entries Enough detail in definitions

(Transitive/Intransitive Verbs, Definite/Indefinite Articles, etc.)

Reasonably standard pages for parsing

METHOD (LEXICON)

METHOD (AGENT COMMUNICATION)

METHOD (AGENT COMMUNICATION)

METHOD (AGENT COMMUNICATION)

EXPERIMENTS AND RESULTS

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

91.67%

82.35%

Percentage Words with Retrieved Data

Proposal Words SBVR Words

EXPERIMENTS AND RESULTS

Min

Max

Ave.

Median

Mode

0 1 2 3 4 5 6

Number of Categories per Word

Proposal Word List SBVR Word List

EXPERIMENTS AND RESULTS

0

1

2

3

4

5

0% 10% 20% 30% 40% 50% 60% 70%

Frequencies for No. Categories per Word

Proposal Word List SBVR Word List

EXPERIMENTS AND RESULTS

Words w/ Correct Cat.

Words w/ Additional cat.

Additional Cat.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Correct and Additional Categories

Proposal Word List SBVR Word List

SUMMARY AND CONCLUSIONS It works!

How well?

High percentage of words had data retrieved, however, too much unnecessary data reduces the effectiveness

Accuracy is impacted by many factors Incomplete/incorrect parsing of the web page Small SBVR specification sample SBVR keywords

Believe it is worth pursuing and improving Fix parsing, use multiple sources Define keyword lexicons, dynamically generate rest Fill in gaps/cull using words with only one category Etc.

LIMITATIONS AND FUTURE WORK

Choice of dictionary Potentially use multiple data sources

Joint words, i.e. most SBVR key words Implementation not perfect

Parsing of the data source No synonyms

Communication Protocol Errors in adjusting association strengths

Strength adjustment values and threshold values used for lexicon classifiers need more research to find more appropriate values

Etc.

REFERENCES

Kleiner, M, Albert, P & Bézivin, J 2009, ‘Configuring Models for (Controlled) Languages’, in Proceedings of the IJCAI–09 Workshop on Configuration (ConfWS–09), Pacadena, CA, USA, pp. 61-68.

Farlex 2010, The Free Dictionary, viewed 11 September 2010, <www.thefreedictionary.com>.

Steels, L & Hanappe, P 2006, ‘Interoperability Through Emergent Semantics A Semiotic Dynamics Approach’, in Journal on Data Semantics VI, vol. 4090, Springer Berlin / Heidelberg, pp. 143-167.