Experiments with Different Indexing Techniques for Text Retrieval tasks on Gujarati Language using Bag of Words Approach
This work addresses information retrieval for Gujarati, a morphologically rich language, but is incremental as it applies standard techniques to a new language.
The paper tackles text retrieval for Gujarati documents using bag-of-words models, showing that techniques like stop word removal and stemming significantly improve mean average precision compared to a baseline.
This paper presents results of various experiments carried out to improve text retrieval of gujarati text documents. Text retrieval involves searching and ranking of text documents for a given set of query terms. We have tested various retrieval models that uses bag-of-words approach. Bag-of-words approach is a traditional approach that is being used till date where the text document is represented as collection of words. Measures like frequency count, inverse document frequency etc. are used to signify and rank relevant documents for user queries. Different ranking models have been used to quantify ranking performance using the metric of mean average precision. Gujarati is a morphologically rich language, we have compared techniques like stop word removal, stemming and frequent case generation against baseline to measure the improvements in information retrieval tasks. Most of the techniques are language dependent and requires development of language specific tools. We used plain unprocessed word index as the baseline, we have seen significant improvements in comparison of MAP values after applying different indexing techniques when compared to the baseline.