The impact of lemmatization for morphologically-rich languages Abstract Are there ways to improve the performance of language models, beyond increases in size -both in the number of model parameters or in the size of training corpora? Our benchmarks show that another...
Arabic is a complex language for NLP tasks, even for simple ones like lemmatization. There are several reasons for this: Arabic creates words based on roots: for example, the word کتاب (kitab, “book”) is derived from ك ت ب (k t b). Many related words are derived from...
Everything looks promising in the world of bots: big players are pushing platforms to build them (Google, Amazon, Facebook, Microsoft, IBM, Apple), large retail companies are adopting them (Starbucks, Domino’s, British Airways), press is excited about movies becoming...
People who use financial databases are aware of the hardships of ensuring information is structured and legible. Don’t worry! Knowledge graphs are here to help. Data volume, nowadays, continues to grow uncontrolled and those datasets are hard to process and draw...
What is Training Data? Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries. The training data is enriched by data labeling or data annotation, with information about entities, slots…...
Two concepts, one mission: to make machines understand humans. Natural Language Processing (NLP) and Machine Learning (ML) are all the rage right now as techniques that complement each other rather than as NLP vs ML In this post, we will focus on NLP and how it works...
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