Lemmatization

Identify all potential roots (lemmas) of each word in a sentence, using morphological analysis and carefully-curated lexicons. Minimize text ambiguity with our enterprise grade multilingual lemmatization tool. We offer the most complete multilingual morphological dictionaries on the market.

Common problem

How to deal with all the available information?

The amount of data available via search engines (WhatsApp, Airbnb or Netflix) grows more and more every day, and if you want your company to make the most out of it, information retrieval systems you use need to connect with similar meanings and different writings (“bicycle”, “bicycles”).

Available in 50 Languages

List of Available Languages

Afrikaans, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bulgarian, Catalan, Czech, Danish, Dutch, English, Esperanto, Estonian, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Kannada, Kazakh, Korean, Kyrgyz, Macedonian, Malay, Malayalam, Mongolian, Nepali, Norwegian Bokmal, Norwegian Nynorsk, Persian, Portuguese, Punjabi, Russian, Serbian Latinica, Slovak, Spanish, Swahili, Swedish, Tagalog, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, and Zulu.

Higher Accuracy

Multilingual

Broad Coverage (formal & informal)

NLP API Platform

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Common problem

How to deal with all the available information? The amount of data available via search engines (WhatsApp, Airbnb or Netflix) grows more and more every day, and if you want your company to make the most out of it, information retrieval systems you use need to connect with similar meanings and different writings (“bicycle”, “bicycles”).

Solution

Use Lemmatization to provide more accurate results Bitext Lemmatization Service relates words that have the same meaning without being misguided by apparent similar letters. For example, in English, it relates “bicycle” and “bicycles” but not “new” and “news”. How? Lemmatization is the process followed to determine the lemma of each word in a text depending on its intended meaning. The lemma form of a word is used to increase search relevancy and to reduce indexing needs in databases. The main difference with stemming is that lemmatization takes into consideration the context to solve the problem of disambiguation.

Applications

Textual Databases Lemmatization can be used for compact indexing and comprehensive retrieval. Our software can index and search massive volumes of multi-language data accurately and efficiently while maintaining the highest level of data availability and security. Machine Learning algorithms Bitext lemmatization software helps to disambiguate and group words by considering the context. Let’s take the word “book” as an example: depending on the surrounding text it can mean two different things.
  • “I enjoy booking my trips online, it helps me to save money”: In this case, “booking” means “reservation”, the lemma being the verb “book”.
  • “I bought three new books last week on my trip to Dublin”: In this case, “books” refers to a novel, the lemma being the noun “book”.
If the algorithm can treat each of those “book” as different words, the error margin will be lower, therefore, we will increase the accuracy of the results even when using a smaller training corpora. eCommerce Search For any eCommerce site, if a user asks for “One flies over the cuckoo’s nest” it will probably not find the intended book if it is using a stemmer. But if you are using Bitext Lemmatization Service it will analyze “fly” and determine its relationship with “flew” and “flown”, and will add these words to the query, finding the book “One Flew Over the Cuckoo’s Nest”.

Lemmatization

Minimize text ambiguity with our enterprise grade multilingual lemmatization tool. We have the most complete multilingual morphological dictionaries in the market. Identify all potential roots (lemmas) of each word in a sentence, using morphological analysis and carefully-curated lexicons.

Common Problem
Solution
Applications
Features
Example