Hamid Mohammadi

Hamid Mohammadi, Seyed Hossein Khasteh, Tahereh Firoozi et al.

Evaluating the readability of a text can significantly facilitate the precise expression of information in written form. The formulation of text readability assessment involves the identification of meaningful properties of the text regardless of its length. Sophisticated features and models are used to evaluate the comprehensibility of texts accurately. Despite this, the problem of assessing texts' readability efficiently remains relatively untouched. The efficiency of state-of-the-art text readability assessment models can be further improved using deep reinforcement learning models. Using a hard attention-based active inference technique, the proposed approach makes efficient use of input text and computational resources. Through the use of semi-supervised signals, the reinforcement learning model uses the minimum amount of text in order to determine text's readability. A comparison of the model on Weebit and Cambridge Exams with state-of-the-art models, such as the BERT text readability model, shows that it is capable of achieving state-of-the-art accuracy with a significantly smaller amount of input text than other models.

Hamid Mohammadi, Amin Nikoukaran

The importance of an efficient and scalable document similarity detection system is undeniable nowadays. Search engines need batch text similarity measures to detect duplicated and near-duplicated web pages in their indexes in order to prevent indexing a web page multiple times. Furthermore, in the scoring phase, search engines need similarity measures to detect duplicated contents on web pages so as to increase the quality of their results. In this paper, a new approach to batch text similarity detection is proposed by combining some ideas from dimensionality reduction techniques and information gain theory. The new approach is focused on search engines need to detect duplicated and near-duplicated web pages. The new approach is evaluated on the NEWS20 dataset and the results show that the new approach is faster than the cosine text similarity algorithm in terms of speed and performance. On top of that, It is faster and more accurate than the other rival method, Simhash similarity algorithm.