# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 2 Related Work
### Neural NER Approaches  
Language-independent NER models like ours have been proposed in the past without any language-specific setting. [PERSON] et al. [9] introduced neural architectures mainly based on the BiLSTM network that relied on character-level, word-level representations and reported the best results in four languages. [PERSON] et al. [36] proposed a character-based NER model using the BiLSTM network. It is empirically shown that the character-level representation learning is superior compared to word-level representations as the basic input features. [PERSON] et al. [13] used a convolutional neural network (CNN) for character-level representation learning, BiLSTM encoder, and CRF for tag prediction. Empirical results show that their model outperformed state-of-the-art neural models in the Japanese NER. [PERSON] et al. [37] used word representations to tackle the NER problem in the Bengali language, with the hypothesis that word-level representations are useful because they belong to the same NE category, such as the name of an organization in the vector space of embedded words. Their proposed technique outperformed standard baseline approaches that use cluster labels of word representations and gazetteers constructed from Wikipedia.Huang et al. [12] integrated self-attention in the BiLSTM-CRF to address the problem of Chinese NER using character-level and word-level representation learning. Their results show that the model yields an increased F1-score on two different datasets. Moreover, [PERSON] and [PERSON] [11] also introduced character-level representation learning for the Chinese NER task. Their model is mainly based on a self-attention mechanism that dynamically decides how much information to use from character-level or glyph-level components. The empirical results on two datasets show that their proposed model performs better than existing state-of-the-art Chinese NER systems. Our work also relates to language-independent NER approaches [11; 13; 38]. A hybrid NER approach proposed by [39] integrates the Support Vector Machine (SVM), dictionaries of Persian NEs, and grammar rules for the Persian language. Their hybrid model yields comparable performance with existing state-of-the-art NER models in other languages. More recently, [PERSON] et al. [6] proposed a neural model by incorporating the word-level and entity-level contextualized representations, entity-aware self-attention, and bidirectional transformer, which obtain state-of-the-art results. [PERSON] et al. [40] proposed a BiLSTM-based model by incorporating contextual representations and label embedding attention. Their empirical results show the state-of-the-art performance on three benchmark NER datasets. A BiLSTM-based model proposed by [15] tackles the problem of boundary tag sparsity and surpasses the existing state-of-the-art NER systems. These models extract and classify NEs, such as person names, location, organization, date, and others, from the labeled datasets. We propose a CaBiLSTM neural model for NE extraction and classification by considering relations between the entity pairs. Our proposed model relies on joint character-level and word-level contextual representations, which contain task-specific knowledge or the knowledge-base of NEs, a BiLSTM encoder, self-attention, and a CRF decoder.