Hendrik J Groenewald Centre for Text Technology (CTexT™) Research Unit: Languages and Literature in the South African Context North-West University, Potchefstroom

Hendrik J GroenewaldCentre for Text Technology (CTexT™)

Research Unit: Languages and Literature in the South African ContextNorth-West University, Potchefstroom Campus (PUK)

South AfricaE-mail: [email protected]

Using Technology Transfer to Advance Automatic Lemmatisation

for Setswana

31 March 2009; Athens

Hendrik J Groenewald

IntroductionLemmatisation

MethodologyConclusion

Overview


• Introduction• Lemmatisation

– Lemmatisation in Setswana– Lemmatisation in Afrikaans

• Methodology– Memory-based Learning– Architecture– Data– Implementation

• Conclusion




Introduction I

• South Africa has 11 official languages– English has the most HLT resources

• Situation is changing

• SA Government is supporting initiatives to develop core linguistic resources and technologies





Introduction II

• Focus: Using technology transfer for– Improving existing linguistic resources– Fast-tracking development

• Improving an existing Setswana lemmatiser by applying a method developed for Afrikaans





OverviewSetswanaAfrikaans

Lemmatisation: Overview

• Process whereby the inflected forms of a word are converted/normalised under the lemma or base form– swim, swimming, swam -> swim

• Lemmatisation is an important process for many NLP tasks– Information Retrieval– Morphological Analysis






Lemmatisation: Overview

• Not to be confused with Stemming– The process whereby a word is reduced to its

stem by removing both inflectional and derivational morphemes

• Two popular approaches to lemmatisation– Rule-based approach– Statistically/data-driven approach






Lemmatisation: Setswana

• First Automatic Lemmatiser for Setswana developed by Brits (2006)– Found that only stems (and not roots) can act

independently as words– Stems should be accepted as lemmas

• Brits formalised rules for determining lemmas– Implemented as Finite-state transducers

• Accuracy: 62.17% when evaluated on a dataset containing 295 randomly selected words






Lemmatisation: Afrikaans

• 2003: Ragel – Accuracy of 67% when evaluated on a 1,000 word data set

• Disappointing accuracy motivated development of another lemmatiser using a different approach

• New Lemmatiser called Lia – Based on data-driven machine learning method– 73,000 lemma-annotated words– Accuracy 92,8% on new data

• Motivated the application of machine learning methods for lemmatisation in Setswana





Memory-based LearningArchitectureDataImplementation

Methodology: Memory-based Learning• Based on k-NN algorithm

– All instances of a certain problem correspond to points in a n-dimensional space

– Nearest neighbours computed by some form of distance metric

Xq

1-Nearest Neighbour

5-Nearest Neighbours






Methodology: Architecture

Based on k-NN algorithm– All instances of a certain problem correspond to points

in a n-dimensional space– Nearest neighbours computed by some form of

distance metric

Start

Choose Algorithm

Compute Statistics on

Training Data

Store Data in Memory

Classify Evaluation

Data

Generate Lemma

Lemma

Evaluation Data

Training Data

Process

Decision

Data

Key






Methodology: Data

• MBL requires large amounts of data• Only 2,947 lemma-annotated Setswana words

available (Brits’s evaluation set)• 2,947 words are a very small data set in memory-

based learning terms






Methodology: Data

• MBL requires that lemmatisation be performed as a classification task

• Data should consist of feature vectors with assigned class labels– Feature vectors: letters of the word– Class label: Transformation from word to lemma






Methodology: Data

• Deriving class labels– Longest common substring– Indicates the string that needs to be removed, as well as possible

replacement strings during the transformation from word form to lemma– Positions of the character strings that need to be removed are indicated as

L (left) or R (right)– If the word form and lemma are identical, the awarded class is “0”






Methodology: Data

• Deriving classes






Methodology: Implementation• Data

– 90% for training– 10% for evaluation

• First version (default algorithmic parameters)– 46.25% Accuracy

• Parameter optimisation– 58.98%

• Accuracy is below that of the rule-based version of Brits






Methodology: Implementation

• Error analysis indicated obvious mistakes






Methodology: Implementation• Solution: Add class distributions to the output and implement a “back-off” mechanism

• Resulted in a further increase in accuracy: 64.06%





Conclusion• The machine learning-based lemmatiser is only 1.9% more accurate than the

rule-based version• Small in comparison to the 25% increase obtained for Afrikaans• Size of the training data

– 2,652 words compared to 73,000 for Afrikaans

• Increasing the amount of training data will increase the accuracy• Most important result: Technology Transfer





Acknowledgements• The work of Jeanetta H. Brits, performed under

the supervision of Rigardt Pretorius and Gerhard B. van Huyssteen


Documents

Hendrik J Groenewald Centre for Text Technology (CTexT™) Research Unit: Languages and Literature in the South African Context North-West University, Potchefstroom