Artificial Intellegence

Social media users have exponentially increased in recent years, and social media data has become one of the most populated repositories of data in the world. Natural language text is one of the main portions of this data. However, this textual data contains many entities, which increases the ambiguity of the data. Entity linking targets finding entity mentions and linking them to their corresponding entities in an external dataset. Recently, FarsBase has been introduced as the first Persian knowledge graph, containing almost 750,000 entities. In this study, we propose ParsEL, the first unsupervised end-to-end entity linking system specially designed for the Persian language, and utilizes contextual and graph-based features to rank the candidate entities. To evaluate the proposed approach, we publish the first entity linking dataset for the Persian language, created by crawling social media text from some popular Telegram channels and contains 67,595 tokens. The results show ParsEL ...

Most of the data on the web is in the form of natural language, but natural language is highly ambiguous, especially when it comes to the frequent occurrence of entities. The goal of entity linking is to find entity mentions and link them to their corresponding entities in an external knowledge base. Recently, FarsBase was introduced as the first Persian knowledge base with nearly 750,000 entities. This research suggested one of the first end-to-end unsupervised entity linking systems specifically for Persian, using context and graph-based features to rank candidate entities. To evaluate the proposed method, we used the first Persian entity-linking dataset created by crawling social media text from some popular Telegram channels. The ParsEL results show that the F-Score of the input data set is 87.1% and is comparable to any other entity-linking system that supports Persian.

Social media users have exponentially increased in recent years, and social media data has become one of the most populated repositories of data in the world. Natural language text is one of the main portions of this data. However, this textual data contains many entities, which increases the ambiguity of the data. Entity linking targets finding entity mentions and linking them to their corresponding entities in an external dataset. Recently, FarsBase has been introduced as the first Persian knowledge graph, containing almost 750,000 entities. In this study, we propose ParsEL, the first unsupervised end-to-end entity linking system specially designed for the Persian language, and utilizes contextual and graph-based features to rank the candidate entities. To evaluate the proposed approach, we publish the first entity linking dataset for the Persian language, created by crawling social media text from some popular Telegram channels and contains 67,595 tokens. The results show ParsEL ...

The goal of this thesis is to suggest some ideas to improve the experience of computational modeling in socio-political areas. These suggestions are presented in the form of a computational modeling framework.
In this framework, firstly, we have suggested that modelers view the socio-political systems as some complex adaptive systems and then use complexity-related theories to describe socio-political behaviours. The agent-based modeling methodology is also suggested as the appropriate methodology to implement these behaviours.
However, the most novel idea of this framework is to introduce the evolutionary epistemology as the appropriate epistemology to study and deal with socio-political problems. In fact, we have used this epistemology from the area of philosophy of science as a support for our exploratory agent-based modeling.
We believe that this framework will help researchers to use computational techniques in a better and more helpful way in understanding and even solving socio-political problems.