Context Pattern Based Agricultural Named Entity Recognition
Payal Biswas, Aditi Sharan, Ashish Kumar · Research in Computing Science · 2019
Named entity recognition (NER) play a vital role in various application of Natural Language processing.Although a significant work has been done in general and biomedical domain NER, but agriculture domain has been ignored for a long time.Agriculture entity includes name of crops, crop diseases, fertilizers etc. Due to the inapplicability of conventional features which has been used for identifying general named entities, recognizing and extracting the agricultural entities become a rigorous and challenging task.As NER in agriculture domain has not been yet explored a lot, thus building up a NER system for agriculture domain is very recent and vital work.This paper proposes a novel context-based approach to develop a NER system for agriculture domain.The proposed approach employs the context pattern for extracting the required entity of interest.The experiment is carried out in two different genres 1) Word Context Pattern 2) POS context pattern.In word context pattern, merely the cooccurring word tokens corresponding to the required entity is considered.While in Part of Speech (POS) rather than considering the co-occurring word tokens, their POS structure is plied.We have proposed seven part of speech patterns which are most likely to comprise all the instances of required entity of interest.The remarkable point is that the proposed POS patterns have not only device the known agricultural entities but have also extracted out 55 hidden entities from the data set.To boost up the performance of the NER system semantic similarity module has also been exercised.The proposed approach attains an accuracy of 70.45 % and recall of 91.3% which is appreciable as the preparatory work.