The link to the tool trial is: AlKhalil for Disambiguation of Arabic Text
Stemming is the process of reducing inflected or derived words to their base form, or stem. This is done by removing prefixes, suffixes, and other affixes from the word. Stemming is a useful technique for many natural language processing (NLP) tasks, such as information retrieval (IR) and text mining.
Arabic is a morphologically rich language, which means that words can be formed by combining roots and patterns. This makes stemming Arabic words more challenging than stemming words from other languages, such as English.
A new efficient heavy/light stemmer for Arabic has been developed based on the interaction between roots and patterns. This stemmer provides three different outputs:
- Individual root: The stemmer can identify the root of a word. This is useful for tasks such as word clustering and synonym detection.
- Stem: The stemmer can produce a stemmed form of a word. This is useful for tasks such as IR and text mining.
- Combination of stem/root: The stemmer can produce a combination of the stem and root of a word. This is useful for tasks such as machine translation and morphological analysis.
The new stemmer has been evaluated on both Modern Standard Arabic and Classical Arabic. It achieved accuracies of 96.93% and 96.56% on the Quranic corpus “Al-Mus’haf” and NEMLAR corpus, respectively.
Overall, the new stemmer is an efficient and effective tool for Arabic language processing. It can be used for a variety of NLP tasks, such as IR, text mining, machine translation, and morphological analysis.
Here are some additional details about the new stemmer:
- It is based on a rule-based approach.
- It uses a dictionary of Arabic roots and patterns.
- It can handle both Modern Standard Arabic and Classical Arabic.
- It is efficient and accurate.
The new stemmer is a valuable tool for researchers and developers working in the field of Arabic language processing. It can be used to improve the performance of a variety of NLP applications.