Deep Learning based Semantic Similarity Detection using Text Data

Abstract

Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.

Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

“…To reduce complexity and obtain a compact set of frequent high-utility sequential patterns 43 , Huang et al proposed an efficient algorithm called TMKU based on the TargetUM algorithm to discover the top-k Target High-Utility Itemsets (top-k THUIs) in 2023. The research achievements and latest algorithms 44 mentioned have led to significant advancements in FHUPM algorithms, including algorithm innovation, improvement of data structures, and performance enhancement.…”

“…2 . Table 4 presents the original data set from the Pima Indians data set 44 , where the order of attributes represented by the data from left to right corresponds to the order of attributes in Table 3 . On the right side of Table 4 , 1 represents diabetic patients and 0 represents non-diabetic patients.…”

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

“…Te work in reference [21] incorporated frequent itemsets with domain knowledge in the form of a taxonomy to mine negative association rules. Shaheen and Abdullah developed a series of algorithms for diferent felds, such as exploring positive and negative context-based association rules for conventional/characteristic data [24,25], and mining context-based association rules on microbial databases to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve [26][27][28][29]. It should be noted that some contradictory rules may be mined when positive and negative rules are mined simultaneously, such as A ⇒ B and A ⇒ ¬B are both strong rules [30][31][32].…”

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

“…TC is a machine learning challenge that tries to classify new written content into a conceptual group from a predetermined classification collection [1]. It is crucial in a variety of applications, including sentiment analysis [2,3], spam email filtering [4,5], hate speech detection [6], text summarization [7], website classification [8], authorship attribution [9], information retrieval [10], medical diagnostics [11], emotion detection on smart phones [12], online recommendations [13], fake news detection [14,15], crypto-ransomware early detection [16], semantic similarity detection [17], part-of-speech tagging [18], news classification [19], and tweet classification [20].…”