Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text
This work addresses ingredient extraction for recipe analysis, but it appears incremental as it builds on existing Hidden Markov Model approaches.
The authors tackled the problem of extracting ingredients from recipe text by developing a Hidden Markov Model-based mathematical model, achieving high-level accuracy with performance improvements over traditional methods that do not consider unknown words.
Natural Language Processing (NLP) is a branch of artificial intelligence that gives machines the ability to decode human languages. Partof-speech tagging (POS tagging) is a pre-processing task that requires an annotated corpus. Rule-based and stochastic methods showed remarkable results for POS tag prediction. On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high-level accuracy of ingredients extracted from text recipe with performances greater than what traditional methods could make without unknown words consideration.