Stemming and Indexing Strategies for Indic Languages

Information Retrieval (IR) in Indic languages poses unique computational linguistic challenges due to their complex morphological structures. Hindi, Marathi, and Bengali belong to the Indo-Aryan language family but exhibit distinct grammatical features that require specialized strategies for stemming and indexing.

Unlike English, which is primarily an analytical language with limited inflection, Hindi, Marathi, and Bengali are highly inflectional and agglutinative. Words are formed by adding suffixes, prefixes, and postpositions to a root or stem. Without proper stemming, a search engine might treat "" (house), "" (houses), and " " (in the house) as entirely unrelated tokens, leading to poor recall in search results.

Hindi uses a Devanagari script and relies heavily on postpositions. Stemming strategies often utilize rule-based algorithms, such as the Light Stemmer, which removes common suffixes like '', '', '', and ''. Because Hindi grammar allows for complex word combinations, iterative suffix stripping is the most common approach to reach the root word.

Marathi is also written in Devanagari but exhibits higher morphological complexity than Hindi due to its richer case system and gender-based inflections. Marathi stemming requires a more aggressive approach to handle compound words. Hybrid modelscombining dictionary-based lookups with suffix-stripping rulesare generally more effective than simple rule-based stems for Marathi.

Bengali uses the Eastern Nagari script. It is highly agglutinative, where grammatical markers are fused with nouns and verbs. Stemming in Bengali is particularly challenging because of "sandhi" (phonetic fusion at word boundaries). Effective indexing for Bengali often requires a morpho-phonemic analyzer that can decompose words before stripping suffixes.

To optimize search performance for these languages, indexers must choose between different granularities:

• Word-based Indexing: Best for languages with clear spacing, but suffers from low recall if stemming is not applied.
• N-gram Indexing: Breaking words into sequences of characters (e.g., 3-grams). This is highly robust for Indic languages as it helps mitigate errors in spelling and morphological variations without requiring a deep linguistic parser.
• Root/Stem Indexing: The most linguistically accurate method, where only the base form of the word is indexed. This reduces index size significantly.

For high-quality information retrieval in Hindi, Marathi, and Bengali, a one-size-fits-all approach is insufficient. Systems must integrate language-specific stemmers that account for script nuances and grammatical complexity. While rule-based stemming provides a quick solution, integrating n-gram indexing alongside morphological analysis offers the best balance of precision and recall for modern search platforms.