Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions

doi:10.12142/ZTECOM.202302003

Abstract

Abstract:

Semantic communication, as a critical component of artificial intelligence (AI), has gained increasing attention in recent years due to its significant impact on various fields. In this paper, we focus on the applications of semantic feature extraction, a key step in the semantic communication, in several areas of artificial intelligence, including natural language processing, medical imaging, remote sensing, autonomous driving, and other image-related applications. Specifically, we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks, such as text classification, sentiment analysis, and topic modeling. In the medical imaging field, we explore how semantic feature extraction can be used for disease diagnosis, drug development, and treatment planning. In addition, we investigate the applications of semantic feature extraction in remote sensing and autonomous driving, where it can facilitate object detection, scene understanding, and other tasks. By providing an overview of the applications of semantic feature extraction in various fields, this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

Key words: semantic feature extraction, semantic communication, deep learning

DENG Letian, ZHAO Yanru. Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions[J]. ZTE Communications, 2023, 21(2): 11-17.

Figures/Tables 1

References 71

1	LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. Nature, 2015, 521(7553): 436–444. DOI: 10.1038/nature14539 DOI
2	HAMET P, TREMBLAY J. Artificial intelligence in medicine [J]. Metabolism, 2017, 69: S36–S40. DOI: 10.1016/j.metabol.2017.01.011 DOI
3	DICK S. Artificial intelligence [J]. Harvard data science review, 2019, 1(1):1–8. DOI: 10.1162/99608f92.92fe150c DOI
4	BAO J, BASU P, DEAN M K, et al. Towards a theory of semantic communication [C]//IEEE Network Science Workshop. IEEE, 2011: 110–117. DOI: 10.1109/nsw.2011.6004632 DOI
5	LUO X W, CHEN H H, GUO Q. Semantic communications: overview, open issues, and future research directions [J]. IEEE wireless communications, 2022, 29(1): 210–219. DOI: 10.1109/MWC.101.2100269 DOI
6	QIN Z J, TAO X M, LU J H, et al. Semantic communications: principles and challenges [EB/OL]. (2021-12-30)[2023-03-01].
7	XIE H Q, QIN Z J, LI G Y, et al. Deep learning enabled semantic communication systems [J]. IEEE transactions on signal processing, 2021, 69: 2663–2675. DOI: 10.1109/TSP.2021.3071210 DOI
8	YANG W T, DU H Y, LIEW Z, et al. Semantic communications for future Internet: fundamentals, applications, and challenges [J]. IEEE communications surveys & tutorials, 2022, 25: 213–250. DOI: 10.1109/COMST.2022.3223224 DOI
9	CHOWDHARY K R. Natural language processing [M]//Fundamentals of artificial intelligence. New Delhi: Springer India, 2020: 603–649. DOI: 10.1007/978-81-322-3972-7_19 DOI
10	NADKARNI P M, OHNO-MACHADO L, CHAPMAN W W. Natural language processing: an introduction [J]. Journal of the American medical informatics association, 2011, 18(5): 544–551. DOI: 10.1136/amiajnl-2011-000464 DOI
11	HIRSCHBERG J, MANNING C D. Advances in natural language processing [J]. Science, 2015, 349(6245): 261–266. DOI: 10.1126/science.aaa8685 DOI
12	MANNING C D, SCHÜTZE H. Foundations of statistical natural language processing [M]. Cambridge: MIT Press, 1999
13	CHEN X J, JIA S B, XIANG Y. A review: knowledge reasoning over knowledge graph [J]. Expert systems with applications, 2020, 141: 112948. DOI: 10.1016/j.eswa.2019.112948 DOI
14	CHEN Z, WANG Y H, ZHAO B, et al. Knowledge graph completion: a review [J]. IEEE access, 2020, 8: 192435–192456. DOI: 10.1109/ACCESS.2020.3030076 DOI
15	ZOU X H. A survey on application of knowledge graph [J]. Journal of physics: conference series, 2020, 1487(1): 012016. DOI: 10.1088/1742-6596/1487/1/012016 DOI
16	MEDHAT W, HASSAN A, KORASHY H. Sentiment analysis algorithms and applications: a survey [J]. Ain shams engineering journal, 2014, 5(4): 1093–1113. DOI: 10.1016/j.asej.2014.04.011 DOI
17	ZHANG L, WANG S, LIU B. Deep learning for sentiment analysis: a survey [J]. Wiley interdisciplinary reviews: data mining and knowledge discovery, 2018, 8(4): e1253. DOI: 10.1002/widm.1253 DOI
18	HUSSEIN D M E D M. A survey on sentiment analysis challenges [J]. Journal of king Saud university: engineering sciences, 2018, 30(4): 330–338. DOI: 10.1016/j.jksues.2016.04.002 DOI
19	GRIFFITHS T L, STEYVERS M, TENENBAUM J B. Topics in semantic representation [J]. Psychological review, 2007, 114(2): 211–244. DOI: 10.1037/0033-295x.114.2.211 DOI
20	VIGLIOCCO G, VINSON D P. Semantic representation [M]//The oxford handbook of psycholinguistics, GASKELL M G ed. Oxford: Oxford University Press, 2012: 195–216. DOI: 10.1093/oxfordhb/9780198568971.013.0012 DOI
21	ABEND O, RAPPOPORT A. The state of the art in semantic representation [C]//55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2017: 77–89. DOI: 10.18653/v1/p17-1008 DOI
22	VIGLIOCCO G, METEYARD L, ANDREWS M, et al. Toward a theory of semantic representation [J]. Language and cognition, 2009, 1(2): 219–247. DOI: 10.1515/langcog.2009.011 DOI
23	VOULODIMOS A, DOULAMIS N, DOULAMIS A, et al. Deep learning for computer vision: a brief review [J]. Computational intelligence and neuroscience, 2018: 1–13. DOI: 10.1155/2018/7068349 DOI
24	WILEY V, LUCAS T. Computer vision and image processing: a paper review [J]. International journal of artificial intelligence research, 2018, 2(1): 22. DOI: 10.29099/ijair.v2i1.42 DOI
25	PAK M, KIM S. A review of deep learning in image recognition [C]//4th International Conference on Computer Applications and Information Processing Technology (CAIPT). IEEE, 2018: 1–3. DOI: 10.1109/CAIPT.2017.8320684 DOI
26	UCHIDA S. Image processing and recognition for biological images [J]. Development, growth & differentiation, 2013, 55(4): 523–549. DOI: 10.1111/dgd.12054 DOI
27	LONG T, LIANG Z N, LIU Q H. Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition [J]. Science China information sciences, 2019, 62(4): 1–26. DOI: 10.1007/s11432-018-9811-0 DOI
28	THOMA M. A survey of semantic segmentation [EB/OL]. (2016-02-21)[2023-03-01].
29	HAO S J, ZHOU Y, GUO Y R. A brief survey on semantic segmentation with deep learning [J]. Neurocomputing, 2020, 406: 302–321. DOI: 10.1016/j.neucom.2019.11.118 DOI
30	LATEEF F, RUICHEK Y. Survey on semantic segmentation using deep learning techniques [J]. Neurocomputing, 2019, 338: 321–348. DOI: 10.1016/j.neucom.2019.02.003 DOI
31	LU D, WENG Q. A survey of image classification methods and techniques for improving classification performance [J]. International journal of remote sensing, 2007, 28(5): 823–870. DOI: 10.1080/01431160600746456 DOI
32	NATH S S, MISHRA G, KAR J, et al. A survey of image classification methods and techniques [C]//International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014: 554–557. DOI: 10.1109/ICCICCT.2014.6993023 DOI
33	NAZ S, MAJEED H, IRSHAD H. Image segmentation using fuzzy clustering: a survey [C]//6th International Conference on Emerging Technologies (ICET). IEEE, 2010: 181–186. DOI: 10.1109/ICET.2010.5638492 DOI
34	DHANACHANDRA N, CHANU Y J. A survey on image segmentation methods using clustering techniques [J]. European journal of engineering and technology research, 2017, 2(1): 15–20. DOI: 10.24018/ejeng.2017.2.1.237 DOI
35	HE K, MAO B, ZHOU X Y, et al. Knowledge enhanced coreference resolution via gated attention [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023: 2287–2293. DOI: 10.1109/BIBM55620.2022.9995551 DOI
36	HE K, HUANG Y C, MAO R, et al. Virtual prompt pre-training for prototype-based few-shot relation extraction [J]. Expert systems with applications, 2023, 213: 118927. DOI: 10.1016/j.eswa.2022.118927 DOI
37	HE K, WU J L, MA X Y, et al. Extracting kinship from obituary to enhance electronic health records for genetic research [C]//4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task. Association for Computational Linguistics, 2019: 1–10. DOI: 10.18653/v1/w19-3201 DOI
38	WANG H N. Development of natural language processing technology [J]. ZTE technology journal, 2022, 28(2): 59–64. DOI: 10.12142/ZTETJ.202202009 DOI
39	HE K, MAO R, GONG T L, et al. Meta-based self-training and re-weighting for aspect-based sentiment analysis [J]. IEEE transactions on affective computing, 2022, PP(99): 1–13. DOI: 10.1109/TAFFC.2022.3202831 DOI
40	MAO R, LIU Q, HE K, et al. The biases of pre-trained language models: an empirical study on prompt-based sentiment analysis and emotion detection [J]. IEEE transactions on affective computing, 2022, early access: 1–11. DOI: 10.1109/TAFFC.2022.3204972 DOI
41	HE K, YAO L X, ZHANG J W, et al. Construction of genealogical knowledge graphs from obituaries: multitask neural network extraction system [J]. Journal of medical Internet research, 2021, 23(8): e25670. DOI: 10.2196/25670 DOI
42	HUANG Y C, HE K, WANG Y G, et al. COPNER: contrastive learning with prompt guiding for few-shot named entity recognition [C]//29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2022: 2515–2527
43	BAO H, HE K, YIN X M, et al. BERT-based meta-learning approach with looking back for sentiment analysis of literary book reviews [M]//Natural language processing and chinese computing. Cham: Springer International Publishing, 2021: 235–247. DOI: 10.1007/978-3-030-88483-3_18 DOI
44	HE K, MAO R, GONG T L, et al. JCBIE: a joint continual learning neural network for biomedical information extraction [J]. BMC bioinformatics, 2022, 23(1): 549. DOI: 10.1186/s12859-022-05096-w DOI
45	MAO B, JIA C, HUANG Y C, et al. Uncertainty-guided mutual consistency training for semi-supervised biomedical relation extraction [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 2318–2325. DOI: 10.1109/bibm55620.2022.9995416 DOI
46	LI Y F, MA X Y, ZHOU X Y, et al. Knowledge enhanced LSTM for coreference resolution on biomedical texts [J]. Bioinformatics, 2021, 7(17): 2699–2705. DOI: 10.1093/bioinformatics/btab15 DOI
47	AMIGO J M, BABAMORADI H, ELCOROARISTIZABAL S. Hyperspectral image analysis: a tutorial [J]. Analytica chimica acta, 2015, 896: 34–51. DOI: 10.1016/j.aca.2015.09.030 DOI
48	KHAN M J, KHAN H S, YOUSAF A, et al. Modern trends in hyperspectral image analysis: a review [J]. IEEE access, 2018, 6: 14118–14129. DOI: 10.1109/ACCESS.2018.2812999 DOI
49	HENROT S, CHANUSSOT J, JUTTEN C. Dynamical spectral unmixing of multitemporal hyperspectral images [J]. IEEE transactions on image processing, 2016, 25(7): 3219–3232. DOI: 10.1109/TIP.2016.2562562 DOI
50	XU X, SHI Z W. Multi-objective based spectral unmixing for hyperspectral images [J]. ISPRS journal of photogrammetry and remote sensing, 2017, 124: 54–69. DOI: 10.1016/j.isprsjprs.2016.12.010 DOI
51	ZHANG X R, GAO Z Y, JIAO L C, et al. Multifeature hyperspectral image classification with local and nonlocal spatial information via Markov random field in semantic space [J]. IEEE transactions on geoscience and remote sensing, 2018, 56(3): 1409–1424. DOI: 10.1109/TGRS.2017.2762593 DOI
52	ZHANG X R, GAO Z Y, AN J L, et al. Joint multi-feature hyperspectral image classification with spatial constraint in semantic manifold [C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2016: 481–484. DOI: 10.1109/IGARSS.2016.7729119 DOI
53	ZHANG X R, SONG Q, GAO Z Y, et al. Spectral-spatial feature learning using cluster-based group sparse coding for hyperspectral image classification [J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 9(9): 4142–4159. DOI: 10.1109/JSTARS.2016.2593907 DOI
54	NING H Y, ZHANG X R, QUAN D, et al. AUD-net: a unified deep detector for multiple hyperspectral image anomaly detection via relation and few-shot learning [J]. IEEE transactions on neural networks and learning systems, 2022, 99: 1–15. DOI: 10.1109/TNNLS.2022.3213023 DOI
55	WU J L, TANG K W, ZHANG H C, et al. Structured information extraction of pathology reports with attention-based graph convolutional network [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021: 2395–2402. DOI: 10.1109/BIBM49941.2020.9313347 DOI
56	WU J L, ZHANG R N, GONG T L, et al. BioIE: biomedical information extraction with multi-head attention enhanced graph convolutional network [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 2080–2087. DOI: 10.1109/BIBM52615.2021.9669650 DOI
57	HE K, HONG N, LAPALME-REMIS S, et al. Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups [J]. Epilepsy & behavior, 2019, 94: 65–71. DOI: 10.1016/j.yebeh.2019.02.002 DOI
58	LIU Y, WU J L, WEI Y H, et al. AEFNet: adaptive scale feature based on elastic-and-funnel neural network for healthcare representation [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 2043–2050. DOI: 10.1109/BIBM52615.2021.9669339 DOI
59	LI C, XU X D, ZHOU G H, et al. Implementation of national health informatization in China: survey about the status quo [J]. JMIR medical informatics, 2019, 7(1): e12238. DOI: 10.2196/12238 DOI
60	WU J L, DONG Y X, GAO Z Y, et al. Dual attention and patient similarity network for drug recommendation [J]. Bioinformatics, 2023, 39(1): btad003. DOI: 10.1093/bioinformatics/btad003 DOI
61	WU J L, QIAN B Y, LI Y, et al. Leveraging multiple types of domain knowledge for safe and effective drug recommendation [C]//31st ACM International Conference on Information & Knowledge Management. ACM, 2022: 2169–2178. DOI: 10.1145/3511808.3557380 DOI
62	WU J L, MAO A Y, BAO X R, et al. PIMIP: an open source platform for pathology information management and integration [C]//Proceedings of 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 2088–2095. DOI: 10.1109/BIBM52615.2021.9669424 DOI
63	WU J L, ZHANG R N, GONG T L, et al. A precision diagnostic framework of renal cell carcinoma on whole-slide images using deep learning [C]//International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021: 2104–2111. DOI: 10.1109/BIBM52615.2021.9669870 DOI
64	WU J L, ZHANG R N, GONG T L, et al. A personalized diagnostic generation framework based on multi-source heterogeneous data [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 2096–2103. DOI: 10.1109/BIBM52615.2021.9669427 DOI
65	MAO A Y, WU J L, BAO X, et al. A two-stage convolutional network for nucleus detection in histopathology image [C]//IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021: 2051–2058, DOI: 10.1109/BIBM52615.2021.9669344 DOI
66	GAO Z Y, HONG B Y, ZHANG X L, et al. Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image [M]//Medical image computing and computer assisted intervention. Cham: Springer International Publishing, 2021: 299–308. DOI: 10.1007/978-3-030-87237-3_29 DOI
67	GAO Z Y, MAO A Y, WU K F, et al. Childhood leukemia classification via information bottleneck enhanced hierarchical multi-instance learning [J]. IEEE transactions on medical imaging, 2023: early access. DOI: 10.1109/tmi.2023.3248559 DOI
68	GAO Z Y, HONG B Y, LI Y, et al. A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images [J]. Medical image analysis, 2023, 83: 102652. DOI: 10.1016/j.media.2022.102652 DOI
69	GAO Z Y, JIA C, LI Y, et al. Unsupervised representation learning for tissue segmentation in histopathological images: from global to local contrast [J]. IEEE transactions on medical imaging, 2022, 41(12): 3611–3623. DOI: 10.1109/tmi.2022.3191398 DOI
70	GAO Z Y, SHI J B, ZHANG X L, et al. Nuclei grading of clear cell renal cell carcinoma in histopathological image by composite high-resolution network [M]//Medical image computing and computer assisted intervention. Cham: Springer International Publishing, 2021: 132–142. DOI: 10.1007/978-3-030-87237-3_13 DOI
71	WANG G C, ZHANG X R, PENG Z L, et al. MOL: towards accurate weakly supervised remote sensing object detection via multi-view noisy learning [J]. ISPRS journal of photogrammetry and remote sensing, 2023, 196: 457–470. DOI: 10.1016/j.isprsjprs.2023.01.011 DOI

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