Abstract
In Natural Language Parsing, in order to perform sequential labeling and segmenting tasks, a probabilistic framework named Conditional Random Field (CRF) have an advantage over Hidden Markov Models (HMMs) and Maximum Entropy Markov Models (MEMMs). This research work is an attempt to develop an efficient model for shallow parsing which is based on CRF. For training the model, around 1,000 handcrafted chunked sentences of Hindi language were used. The developed model is tested on 864 sentences and evaluation is done by comparing the results with gold data. The accuracy is measured by precision, recall, and F-measure and is found to be 98.04, 98.04, and 98.04, respectively.
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Asopa, S., Asopa, P., Mathur, I., Joshi, N. (2019). A Shallow Parsing Model for Hindi Using Conditional Random Field. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-13-2354-6_31
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DOI: https://doi.org/10.1007/978-981-13-2354-6_31
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