Natural Language Processing Services

Machine learning models are advancing in their ability to read and understand text and audio but still struggle to understand the complexities of language. Our experienced data analyst teams understand the nuance of your business and the subtleties in language required to accurately tag the text you need to train your NLP applications. Whether you’re training a chatbot, legal contract review application, or financial analysis algorithm, our NLP services provide the high-quality data you need to make the models powering your analytical or speech-based applications more accurate.

NLP Managed Workforce

Our data analysts combine your business context with their understanding of language, syntax, and sentence structure to accurately parse and tag text according to your specifications. We can extract meaning from raw audio and text data to advance your NLP project.

Natural Language Processing Expertise

From information extraction to sentiment analysis, we can help you unlock the hidden insights contained within written text and verbal language, powering your NLP algorithms and machine learning models.

Arabic Natural Language Processing Services

Arabic poses a lot of challenges to Natural Language Processing (NLP). Arabic is both morphologically rich and highly ambiguous. In Modern Standard Arabic (MSA), a complete part-of-speech tag set has over 300,000 tags (whereas English has about 50), and MSA words have 12 morphological analyses on average (English has 1.25 POS tags per word on average). The high ambiguity is primarily the result of Arabic orthography, which almost always omits the diacritics used to specify short vowels and consonantal doubling.

Furthermore, Arabic has complex morpho-syntactic agreement rules and a lot of irregular forms: over half of Arabic plurals are irregular (“broken plurals”). Finally, Arabic has a large number of dialectal variants that are as different from MSA as romance languages are different from Latin. MSA is the official form of Arabic, but is no one’s mother tongue. The dialects, the true mother tongues, are primarily spoken, do not have written standards, and have very limited resources.

The following are the multiple projects in CAMeL Lab that address these challenges for Arabic by sub category:

Arabic and Arabic Dialect Orthography

Arabic poses a lot of challenges to Natural Language Processing (NLP). Arabic is both morphologically rich and highly ambiguous. In Modern Standard Arabic (MSA), a complete part-of-speech tag set has over 300,000 tags (whereas English has about 50), and MSA words have 12 morphological analyses on average (English has 1.25 POS tags per word on average). The high ambiguity is primarily the result of Arabic orthography, which almost always omits the diacritics used to specify short vowels and consonantal doubling. Furthermore, Arabic has complex morpho-syntactic agreement rules and a lot of irregular forms: over half of Arabic plurals are irregular (“broken plurals”). Finally, Arabic has a large number of dialectal variants that are as different from MSA as romance languages are different from Latin. MSA is the official form of Arabic, but is no one’s mother tongue.