ACO-tagger: A Novel Method for Part-of-Speech Tagging using Ant Colony Optimization

Swarm Intelligence algorithms have gained significant attention in recent years as a means of solving complex and non-deterministic problems. These algorithms are inspired by the collective behavior of natural creatures, and they simulate this behavior to develop intelligent agents for computational tasks. One such algorithm is Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants and their pheromone laying mechanism. ACO is used for solving difficult problems that are discrete and combinatorial in nature. Part-of-Speech (POS) tagging is a fundamental task in natural language processing that aims to assign a part-of-speech role to each word in a sentence. In this research paper, proposed a high-performance POS-tagging method based on ACO called ACO-tagger. This method achieved a high accuracy rate of 96.867%, outperforming several state-of-the-art methods. The proposed method is fast and efficient, making it a viable option for practical applications.

Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses

The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.

Persian Abstract Meaning Representation

Abstract Meaning Representation (AMR) is an annotation framework representing the semantic structure of a sentence as a whole. From the beginning, AMR was not intended to act as an interlingua; however, it has made progress towards the idea of designing a universal meaning representation framework. Accordingly, developing AMR annotation guidelines for different languages, based on language divergences, is of significant importance. In this paper, we elaborate on Persian Abstract Meaning Representation (PAMR) annotation specifications, based on which we annotated the Persian translation of "The Little Prince" as the first gold standard for Persian AMR. Moreover, we describe how some Persian-specific syntactic constructions would result in different AMR annotations.

Improving Information Retrieval Results for Persian Documents using FarsNet

In this paper, we propose a new method for query expansion, which uses FarsNet (Persian WordNet) to find similar tokens related to the query and expand the semantic meaning of the query. For this purpose, we use synonymy relations in FarsNet and extract the related synonyms to query words. This algorithm is used to enhance information retrieval systems and improve search results. The overall evaluation of this system in comparison to the baseline method (without using query expansion) shows an improvement of about 9 percent in Mean Average Precision (MAP).