Language stemming is a method used by search engines to improve relevancy. Precisely, it is the process of extracting 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 important for any search engine, especially when that search engine has users who speak different languages.

This article will outline what language specific stemming in 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.”

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

Why Is Language-Specific Stemming Important?

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

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 “the dog is running toward the fence,” 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 “dog” or “fence.” This could result in a less relevant search experience for the user.

Ways To Achieve Language-Specific Stemming

• Natural Language Processing (NLP).

NLP is the process of converting 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. This means the search engine must be able to understand what the user is trying to find.

Stemming is just one part of NLP. Other components include tokenization, parsing, and semantic analysis. For a search engine to achieve true language-specific stemming, it must be able to handle the complexities of human language.

• Grammar and Syntax.

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

• Existing Stemmers.

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

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 able to reduce the word “Hund” to “Hund” since German doesn’t use the same root word.

This means stemming can’t be used as a standalone feature. It needs to be implemented in conjunction with other language processing features.