# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 1 Introduction  
The Named Entity Recognition system recognizes named entities (NEs) and classifies them into predefined categories, such as a person, location, organization, and time [1]. It is used as the first step in question answering [2], information retrieval [1], text summarization [3], machine translation [4], and more [5]. A series of neural NER models have been proposed over the past decade for English [6, 7, 8, 9], Chinese [10, 11, 12], Japanese [13], Urdu [4, 14], and multilingual systems [6, 15], which have achieved state-of-the-art performance. The NER task in Asian languages [16] has recently attracted many researchers due to its importance and widespread NLP applications. Still, Sindhi has not been studied well mainly because of the unavailability of labeled datasets [17, 18]. Sindhi has a complex morphological and syntactical structure [19] that originates from Arabic, Hindi, Sanskrit, and Persian [18, 20]. Context sensitivity in writing and comprehension are the its main characteristics [21]. Unlike other languages, Sindhi has spelling variations, ambiguities in suffixes, and different writing styles (inclusion and exclusion of space), which further contribute to increasing the difficulty in language processing and the NER [17] task. The NER in the English language has significantly benefited from its capitalization rule, part-of-speech tagging, and availability of otherlanguage resources [4]. On the contrary, Sindhi has no capitalization rule, which makes difference between plain text and NEs [17]. Conversely, digital processing also suffers from the scarcity of part-of-speech tagger and gazetteer resources [18]. Additionally, morphological richness in Sindhi leads to ambiguities, which make the NER task more challenging as compared to western languages [7]. We summarize the challenges related to Sindhi NER and characteristics in Table 1. Some of these ambiguities may also be found in other Asian languages, such as Urdu [4]. More recently, NN-based models have attained a state-of-the-art status in NER [6; 15; 22; 23] and other sequence tagging tasks [8; 24]. These models primarily rely on the feature representation at character-level [10; 11], word-level [25; 26], and joint character-level and word-level [9; 13] to encode a word boundary and its context. Moreover, task-specific neural representation learning [27; 28] has been widely used to boost the performance of NER  
\begin{table}
**Ambiguity** & **Description** \\ \hline Lack of capitalization & The capitalization rule in the English language is an important characteristic to enhance the performance of the NER system. However, there is no difference between NEs and plain text in Sindhi language. Thus, NER becomes challenging task. \\ \hline Context sensitivity & 1. Due to change in the context, a token in a sentence that refers to an NE may change its type or may not remain an NE. Such as the name of a girl \(\mathcal{S}^{\texttt{Silve}}\)(pronounced as _Sindhu_) is labeled as PERSON, whereas it also refers to the name of the river labeled as LOC (location) in the SiNER dataset. However, as a verb (pronounced as _Sandhou_), which means ’partition’, it does not belong to NE. \\  & 2. Ambiguities in the person names are also common. An example of a person’s name (_[PERSON]-\(\mathcal{Y}\),\(\mathcal{Y}\),\(\mathcal{Y}\)_) is also tagged as the TITLE of a person because it also means minister. The name of a girl (_[PERSON]-\(\mathcal{Y}\)_) is also an adjective, which means beautiful. \\  & 3. The country name Syria (_Shaam-\(\mathcal{S}^{\texttt{L}}\)_) tagged as CPE (geopolitical entity) is also a name of person (_[PERSON]-\(\mathcal{S}^{\texttt{L}}\)_), and it also means evening. \\ \hline \hline \end{tabular} \