Comparative Analysis: Intelligent Systems and NLP Applications

This essay provides a comparative summary of several research papers focusing on Natural Language Processing (NLP) within the context of intelligent systems. It analyzes “A bibliometric analysis of natural language processing in medical research” by Chen et al., which explores NLP's role in medical studies, and contrasts it with “Dynamic Memory Networks for Natural Language Processing” by Kumar et al., which examines the efficiency of Dynamic Memory Networks (DMN) for question answering. Additionally, it considers the implications of Fuzzy Logic in NLP for web searching, as presented by Gupta, Jain, and Joshi, and the use of NLP techniques in computer vision for image recognition, as described by Socher et al. The analysis concludes that while the medical study offers limited implementable significance, the Dynamic Memory Networks model by Kumar et al. presents tangible outputs for advancing data processing and AI remodeling. The essay highlights the varying degrees of impact and applicability of these different approaches to NLP.

Running head: COMPARATIVE SUMMARY
Intelligent Systems and Natural Language Processing
Name of the Student
Name of the University
Author Note

1COMPARATIVE SUMMARY
Natural Language Processing or NPL has made a huge change in Artificial
Intelligence and Human Language Recognition and Processing System. In “A bibliometric
analysis of natural language processing in medical research”, the author Chen et al. presented
a bibliometric analysis based on the outputs of global medical studies and research on the
topic NPL. On the other hand the in the article “Dynamic Memory Networks for Natural
Language Processing” author Kumar et al. examined the efficiency of existing Dynamic
Memory Networks (DMN) for NPL used for question answers. At the same time, Gupta, Jain
and Joshi presented the implications of Fuzzy Logic in NPL in web searching with User
Inputs Processing Efficiency (UIPE). Author Socher et al. described the usage of NLP
techniques in computer vision considering image recognition and processing system.
The presentation of Chen et al. is only covered the NPL studies in medical field
aiming to identify the empowerment of NPL in medical use. Hence, it is clear that this study
does not have any implementable significance in the developmental study of NPL. On the
contrary, the Dynamic Memory Networks proposed by author Kumar et al. has changed the
perception of user input decoding and processing concepts. This model has shown some
significant increment in operational efficiency of episodic memory module and Attention
mechanism by comparing the time required to process a question ( ). G has been
declared as scoring function. Gupta, Jain and Joshi focuses on just the efficiency assessment
of existing fuzzy logic and future implication. The research of Socher et al. presented an
algorithm, which is only effective for image recognition and processing through adjustment
matrix. This algorithm has been successfully executed the image fragmentation processing by
4% increased efficiency. The “Structural Perdition” system with Recursive Neural Network
developed the correct trees of data to recognize and process the image based lingual
information.

2COMPARATIVE SUMMARY
As per all the critical comparison of presented algorithms on NPL, the paper of NPL
research in medical studies has been found as the least significant paper among the others.
The Structural Perdition in a neural network shows an efficient way of image
decoding/encoding procedure. Similarly, However, the study presented by Kumar et al. has
the most tangible and implementable outputs that can be directly used for advance data
processing and AI remodeling through “G scoring” system.

3COMPARATIVE SUMMARY
Reference:
Chen, X., Xie, H., Wang, F. L., Liu, Z., Xu, J., & Hao, T. (2018). A bibliometric analysis of
natural language processing in medical research. BMC medical informatics and
decision making, 18(1), 14.
Gupta, C., Jain, A., & Joshi, N. (2018). Fuzzy Logic in Natural Language Processing–A
Closer View. Procedia Computer Science, 132, 1375-1384.
Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., ... & Socher, R.
(2016, June). Ask me anything: Dynamic memory networks for natural language
processing. In International Conference on Machine Learning (pp. 1378-1387).
Socher, R., Lin, C. C., Manning, C., & Ng, A. Y. (2011). Parsing natural scenes and natural
language with recursive neural networks. In Proceedings of the 28th international
conference on machine learning (ICML-11) (pp. 129-136).