A hybrid entity-centric approach to Persian pronoun resolution

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this purpose, seven high-precision rule-based sieves are designed for the Persian language. Then, a random forest classifier links pronouns to the previous partial clusters. The presented method demonstrates exemplary performance using pipeline design and combining the advantages of machine learning and rulebased methods. This method has solved some challenges in end-to-end models. In this paper, the authors develop a Persian coreference corpus called Mehr in the form of 400 documents. This corpus fixes some weaknesses of the previous corpora in the Persian language. Finally, the efficiency of the presented system compared to the earlier model in Persian is reported by evaluating the proposed method on the Mehr and Uppsala test sets.

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.

Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach

In late 2019, COVID-19, a severe respiratory disease, emerged, and since then, the world has been facing a deadly pandemic caused by it. This ongoing pandemic has had a significant effect on different aspects of societies. The uncertainty around the number of daily cases made it difficult for decision-makers to control the outbreak. Deep Learning models have proved that they can come in handy in many real-world problems such as healthcare ones. However, they require a lot of data to learn the features properly and output an acceptable solution. Since COVID-19 has been a lately emerged disease, there was not much data available, especially in the first stage of the pandemic, and this shortage of data makes it challenging to design an optimized model. To overcome these problems, we first introduce a new dataset with augmented features and then forecast COVID-19 cases with a new approach, using an evolutionary neural architecture search with Binary Bat Algorithm (BBA) to generate an optimized deep recurrent network. Finally, to show our approach's effectiveness, we conducted a comparative study on Iran's COVID-19 daily cases. The results prove our approach's capability to generate an accurate deep architecture to forecast the pandemic cases, even in the early stages with limited data.