A Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity Recognition

Abstract

The Named Entity Recognition (NER) is an integrated task in many NLP applications such as machine translation, Information extraction and question answering. Arabic is one of the authorised spoken languages in the united nation. Currently, there is much Arabic information on the internet, so, nowadays the need for tools which process this information becomes significant. In this study, we have examined the impact of the conditional random field and the structured support vector machine in the task of Arabic NER. The structured support vector machine is the first time to be applied in the Arabic name entity recognition. Our proposed system has three stages: Preprocessing, extracting features and building model. We have used simple features like the bag of words in the [-1,1] window, the bag of part of speech tag in the [-1,1] window to enable our system to detect the multi-words entities. Also, we have tried to enhance the Stanford part of speech tagger to enhance the tagger output tags, which enabled our system to differentiate between the name entities from the nonentities. In addition, we have employed the binary features of: Is a person, is a prename, is a pre-location, is a location and is an organization. Our system has been trained and tested on part of ANER Crop. The results have proved that the conditional random field-based Arabic NER system outperforms the structured support vector machine-based Arabic NER using the same features set.

Towards an approach based on particle swarm optimization for Arabic named entity recognition on social media

“…Research on NER tasks has shown that the SVM-based NER task needed less time and performed better than the CRF-based NER task to recognize named entities when applying similar features [35]. For that perpose we create the baseline to evaluate our proposed approach for each dataset.…”

Banking System Incidents Analysis Using Knowledge Graph

“…Wigington et al showed that CTC is not suitable for multilabel tasks and presented a novel Multi-Label Connectionist Temporal Classification (MCTC) loss function for multi-label, sequence-to-sequence classification (Wigington et al, 2019). Muhammad et al examined the impact of the conditional random field and the structured support vector machine in the task of Arabic NER (Muhammad et al, 2020). Asgari-Chenaghlu et al proposed two novel deep learning approaches utilizing multimodal deep learning and Transformers (Asgari-Chenaghlu et al, 2020).…”