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Language stemming is a method used by search engines to improve relevancy. At its core, this process extracts the root word from a set of words. For example, stemming the word “narrative” returns “narr” as its root.

Processing natural language queries accurately and efficiently is critical for any e-commerce search engine, especially when that search engine has users who speak different languages.

This article will outline what language-specific stemming in e-commerce search engines is and why it’s so important.

What Is Language-Specific Stemming?

Stemming is the first step any search engine takes when processing natural language queries. By reducing complex words to their roots, search engines can find and reveal the most relevant results for a user.

There are two types of stemming: lexical and linguistic.

Lexical stemming reduces each word to one root and can change depending on the language and context of the word. For example, the word “running” can be reduced to the root “run” when it’s used in the sentence “the dog is running.”

Lexical stemming is a common feature in any language-aware search engine. Linguistic stemming uses rules based on grammar and syntax to reduce words. For example, the word “running” is reduced to “run” when it’s used in the sentence “the dog is running towards the fence.”

It allows linguistic stemming to handle proper nouns and other words that don’t follow a standard lexical pattern. Linguistic stemming is less common yet it’s essential to achieving language-specific stemming.

Yet, there is still a need for further improvements in languages with limited resources. [1].

Why Is Language-Specific Stemming Important?

The search engine needs to be able to rank results that are relevant to the user’s query. That is where language-specific stemming becomes essential.

Search engines are more likely to match a user’s query with results if they’re able to understand and match it with a word in the query. If a user searches for “running shoes for trail running,” the search engine might return results with the word “running” because it’s more common.

Search engines that perform lexical stemming are more likely to get a match, but they’re still missing out on documents that contain words like “trail” or “shoes.” This could result in a less relevant search experience for the user.

Achieving true language-specific stemming requires understanding the complexities of human language, including its grammar and syntax. Stemming can improve information retrieval accuracy and performance. [2].

Ways To Achieve Language-Specific Stemming

  • Natural Language Processing (NLP).

Natural language processing (the critical feature in conversion-boosting e-commerce search solutions like LupaSearch) converts unstructured data like text into structured data that machines can understand.

Any search engine that supports a language must also be able to process that language. It means the search engine must be able to understand what the user is trying to find.

This way, with LupaSearch, your website users can search the way they speak (using jargon, informal expressions, etc.), and the search engine will return the user-relevant products. That is the power of a converting e-commerce search.

For a search engine to achieve true language-specific stemming, it must be able to handle the complexities of human language. It involves tokenization, parsing, and semantic analysis alongside stemming [3].

  • Grammar and Syntax.

One approach to achieving language-specific stemming is to use rules based on grammar and syntax. It allows the search engine to process words more than a human would. It is especially important when dealing with proper nouns, which don’t follow a standard lexical pattern.

Utilizing rules based on grammar and syntax allows the search engine to process words in a way that mimics human understanding, essential for handling proper nouns and irregular words. Bilingual term lists and parallel corpora improve cross-lingual information retrieval performance, while stemming can hinder performance in highly inflected languages like Arabic [4].

  • Existing Stemmers.

Some stemmers have already been created and are available for use. Different language stemmers can be used to achieve language-specific stemming.

Leveraging pre-developed stemmers can facilitate language-specific stemming across various languages. Malay stemming algorithms can improve information retrieval and knowledge management by reducing words to their roots, but further improvements are needed by applying background knowledge like root word dictionaries [5].

Limitations of Language-Specific Stemming

Stemming is limited by the language it’s supporting. For example, a stemmer that supports both English and German would only be able to reduce the word “dog” to “dog.” It wouldn’t be possible to reduce the word “Hund” to “Hund” since German doesn’t use the same root word.

It means stemming can’t be used as a standalone feature. It needs to be implemented in conjunction with other language processing features to ensure the search engine can accurately process and understand queries in different languages [6].

An e-commerce search that deeply understands your users’ language

If you are looking for a leading e-commerce search solution that would support language-specific stemming, NLP, and other linguistic solutions, look no further than LupaSearch.

LupaSearch is a go-to e-commerce search provider that understands even the most abstract user queries and matches them with user-relevant product suggestions.

Make the most out of your e-commerce search.

Request a free demo, see how e-commerce search works on your website, and witness your business growth.


  1. Dave, N., Mehta, M., & Kotecha, K. (2023). A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages. ACM Transactions on Asian and Low-Resource Language Information Processing. doi: 10.1145/3604612.
  2. Moral, C., Jiménez, A., Imbert, R., & Ramírez, J. (2014). A survey of stemming algorithms in information retrieval. Inf. Res., 19.
  3. Chau, M., Qin, J., Zhou, Y., Tseng, C., & Chen, H. (2008). SpidersRUs: Creating specialized search engines in multiple languages. Decis. Support Syst., 45, 621-640. doi: 10.1016/j.dss.2007.07.006.
  4. Xu, J., & Weischedel, R. (2005). Empirical studies on the impact of lexical resources on CLIR performance. Inf. Process. Manag., 41, 475-487. doi: 10.1016/j.ipm.2004.06.009.
  5. Alfred, R., Leong, L., On, C., & Anthony, P. (2013). A Literature Review and Discussion of Malay Rule - Based Affix Elimination Algorithms. , 285-297. doi: 10.1007/978-94-007-7287-8_23.
  6. Nasra, I., & Maree, M. (2017). On the use of Arabic stemmers to increase the recall of information retrieval systems. 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2462-2468. doi: 10.1109/FSKD.2017.8393161.