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Efficient Estimation of Word Representations in Vector Space

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Distributed Representations of Words and Phrases and their Compositionality

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Distributional Structure

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Software Framework for Topic Modelling with Large Corpora

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Natural language processing with Python

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Deep Learning for Anomaly Detection: A Survey

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Detecting formal thought disorder by deep contextualized word representations

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A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models

Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders

Corcoran, C. M., & Cecchi, G. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.06.004 · Cited by 157

Considerations about learning Word2Vec

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A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders

Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96

Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet

Orkphol, K., & Wu, Y. J. (2019) · Future Internet · doi:10.3390/fi11050114 · Cited by 91

The Semantic Scale Network: An online tool to detect semantic overlap of psychological scales and prevent scale redundancies.

Rosenbusch, H., Wanders, F., & Pit, I. L. (2019) · Psychological Methods · doi:10.1037/met0000244 · Cited by 84

Short text similarity measurement methods: a review

Prakoso, D. W., Abdi, A., & Amrit, C. (2021) · Soft Computing · doi:10.1007/s00500-020-05479-2 · Cited by 72

Building a soft skill taxonomy from job openings

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Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

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Artificial Intelligence-Generated and Human Expert-Designed Vocabulary Tests: A Comparative Study

Yunjiu, L., Wei, W., & Zheng, Y. (2022) · SAGE Open · doi:10.1177/21582440221082130 · Cited by 49

Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

Bauman, K., & Tuzhilin, A. (2021) · Information Systems Research · doi:10.1287/isre.2021.1036 · Cited by 38

DeepMove: Learning Place Representations through Large Scale Movement Data

Zhou, Y., & Huang, Y. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622444 · Cited by 36

Does Finance Benefit Society? A Language Embedding Approach

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Arabic Fake News Detection in Social Media Context Using Word Embeddings and Pre-trained Transformers

Azzeh, M., Qusef, A. D., & Alabboushi, O. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-08959-x · Cited by 37

A Method of Short Text Representation Based on the Feature Probability Embedded Vector

Zhou, W., Wang, H., Sun, H., & Sun, T. (2019) · Sensors · doi:10.3390/s19173728 · Cited by 30

Development of the set models and a method to form information spaces of scientific activity subjects for the steady development of higher education establishments

Biloshchytskyi, A. O., Kuchansky, A., Andrashko, Y., Omirbayev, S., Mukhatayev, A., Faizullin, A., & Toxanov, S. (2021) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2021.233655 · Cited by 30

An Efficient Text Classification Using fastText for Bahasa Indonesia Documents Classification

Amalia, A., Sitompul, O. S., Nababan, E. B., & Mantoro, T. (2020) · International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) · doi:10.1109/databia50434.2020.9190447 · Cited by 33

DeepPPF: A deep learning framework for predicting protein family

Yusuf, S. M., Zhang, F., Zeng, M., & Li, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.062 · Cited by 29

Exploring sentiment analysis in handwritten and E-text documents using advanced machine learning techniques: a novel approach

Ahamad, R., & Mishra, K. N. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01064-2 · Cited by 33

The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences

Elce, V., Handjaras, G., & Bernardi, G. (2021) · Clocks & Sleep · doi:10.3390/clockssleep3030035 · Cited by 30

Artificial Intelligence Scientific Documentation Dataset for Recommender Systems

Ortega, F., Bobadilla, J., Gutiérrez, A., Hurtado, R., & Li, X. (2018) · IEEE Access · doi:10.1109/access.2018.2867731 · Cited by 27

On the Various Semantics of Similarity in Word Embedding Models

Elekes, Á., Schäler, M., & Böhm, K. (2017) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2017.7991568 · Cited by 27

Corpus Specificity in LSA and Word2vec: The Role of Out-of-Domain Documents

Altszyler, E., Sigman, M., & Slezak, D. F. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3001 · Cited by 24

Answering with Cases: A CBR Approach to Deep Learning

Amin, K., Kapetanakis, S., Althoff, K., Dengel, A. R., & Petridis, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01081-2_2 · Cited by 24

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Thakur, D. S., Singh, J., Dhiman, G., Shabaz, M., & Gera, T. (2021) · Complexity · doi:10.1155/2021/4551067 · Cited by 22

Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA

Jorge‐Botana, G., Olmos, R., & Luzón, J. M. (2019) · Cognitive Processing · doi:10.1007/s10339-019-00934-x · Cited by 21

Advances in Machine Learning for the Behavioral Sciences

Kliegr, T., Bahník, Š., & Fürnkranz, J. (2019) · American Behavioral Scientist · doi:10.1177/0002764219859639 · Cited by 22

shinyReCoR: A Shiny Application for Automatically Coding Text Responses Using R

Andersen, N., & Zehner, F. (2021) · Psych · doi:10.3390/psych3030030 · Cited by 20

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Alsaid, A., Li, M., Chiou, E. K., & Lee, J. D. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1192020 · Cited by 23

Toward meaningful notions of similarity in NLP embedding models

Elekes, Á., Englhardt, A., Schäler, M., & Böhm, K. (2018) · International Journal on Digital Libraries · doi:10.1007/s00799-018-0237-y · Cited by 17

Morpheme Embedding for Bahasa Indonesia Using Modified Byte Pair Encoding

Amalia, A., Sitompul, O. S., Mantoro, T., & Nababan, E. B. (2021) · IEEE Access · doi:10.1109/access.2021.3128439 · Cited by 15

On the various semantics of similarity in word embedding models

Elekes, Á., Schäler, M., & Böhm, K. (2017) · doi:10.5555/3200334.3200350 · Cited by 15

The Internet Never Forgets: A Four-Step Scraping Tutorial, Codebase, and Database for Longitudinal Organizational Website Data

Haans, R. F. J., & Mertens, M. J. (2024) · Organizational Research Methods · doi:10.1177/10944281241284941 · Cited by 14

The Linguistic Analysis of Scene Semantics: LASS

Rose, D. J., & Bex, P. J. (2020) · Behavior Research Methods · doi:10.3758/s13428-020-01390-8 · Cited by 14

Comparison of Topic Modeling Methods for Type Detection of Turkish News

Güven, Z. A., Di̇ri̇, B., & Çakaloğlu, T. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8907050 · Cited by 13

Exploring semantic differences between the Indonesian prefixesPE-andPEN-using a vector space model

Denistia, K., Shafaei-Bajestan, E., & Baayen, R. H. (2021) · Corpus Linguistics and Linguistic Theory · doi:10.1515/cllt-2020-0023 · Cited by 12

Using contextual information for automatic triage of posts in a peer-support forum

Altszyler, E., Berenstein, A. J., Milne, D., Calvo, R. A., & Slezak, D. F. (2018) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w18-0606 · Cited by 12

TBMM Genel Kurul Tutanaklarından Yakın Anlamlı Kavramların Çıkarılması

Polat, H., & Körpe, M. (2018) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.402468 · Cited by 11

The Centroid Cannot Hold: Comparing Sequential and Global Estimates of Coherence as Indicators of Formal Thought DisorderPreprint

Xu, W., Portanova, J., Chander, A., Ben‐Zeev, D., & Cohen, T. (2020) · doi:10.31234/osf.io/sfkqc · Cited by 11

Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning

Cheon, S., Methiyothin, T., & Ahn, I. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282119 · Cited by 10

Neural variational entity set expansion for automatically populated knowledge graphs

Rastogi, P., Poliak, A., Lyzinski, V., & Durme, B. V. (2018) · Information Retrieval · doi:10.1007/s10791-018-9342-1 · Cited by 10

Evaluating the predication model of metaphor comprehension: Using word2vec to model best/worst quality judgments of 622 novel metaphors

Harati, P., Westbury, C. F., & Kiaee, M. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01558-w · Cited by 10

Representing EHRs with Temporal Tree and Sequential Pattern Mining for Similarity Computing

Pokharel, S., Zuccon, G., & Li, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65390-3_18 · Cited by 9

Bigger does not mean better! We prefer specificity.

Dusserre, E., & Padró, L. (2017) · Cited by 9

Reading development at the text level: an investigation of surprisal and embedding-based text similarity effects on eye-movements in Chinese early readers

Fan, X., & Reilly, R. (2020) · Journal of Eye Movement Research · doi:10.16910/jemr.13.6.2 · Cited by 8

Analyzing semantic similarity amongst textual documents to suggest near duplicates

Devarajan, V., & Subramanian, R. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v25.i3.pp1703-1711 · Cited by 8

Comparative analysis with topic modeling and word embedding methods after the Aegean Sea earthquake on Twitter

Eligüzel, N., Çetinkaya, C., & Dereli, T. (2022) · Evolving Systems · doi:10.1007/s12530-022-09450-4 · Cited by 8

Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit

García-Rudolph, A., Saurí, J., Cegarra, B., & Guitart, M. B. (2020) · JMIR Medical Informatics · doi:10.2196/17903 · Cited by 7

Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval

Ltaifa, I. B., Hlaoua, L., & Romdhane, L. B. (2020) · Cybernetics & Systems · doi:10.1080/01969722.2019.1705548 · Cited by 8

From natural language to accounting entries using a natural language processing method

Chen, Y., Huang, X. Z., & Wu, Z. (2023) · Accounting and Finance · doi:10.1111/acfi.13067 · Cited by 7

Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks

Ezeani, I., Onyenwe, I., & Hepple, M. (2018) · Lancaster EPrints (Lancaster University) · Cited by 7

Differences in syntactic and semantic analysis based on machine learning algorithms in prodromal psychosis and normal adolescents

Malik, K., Widyarini, I. G. A. A., Kaligis, F., Kusumawardhani, A., Yusuf, P. A., Krisnadhi, A. A., Riandi, O., & Pujitresnani, A. (2023) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2023.103633 · Cited by 6

Behaviour Analysis Using Word Embedding & Machine Learning on Social Media

Jarang, S., Joshi, D., & Deshpande, V. (2019) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea47591.2019.9129273 · Cited by 6

Imagetic and affective measures of memory reverberation diverge at sleep onset in association with theta rhythm

Mota, N. B., Soares, E. S., Altszyler, E., Sánchez-Gendriz, I., Muto, V., Heib, D. P. J., Slezak, D. F., Sigman, M., Copelli, M., Schabus, M., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119690 · Cited by 5

The Novel in the Spanish Silver Age

Tello, J. C. (2021) · Digital humanities research · doi:10.14361/9783839459256 · Cited by 5

Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics

Valentini, F., Sosa, J., Slezak, D. F., & Altszyler, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.9 · Cited by 6

Mythologizing war: legacies of conflict in Croatian parliamentary debates

Mochťak, M. (2020) · Journal of Southeast European and Black Sea Studies · doi:10.1080/14683857.2020.1800283 · Cited by 5

The Novel in the Spanish Silver Age

Tello, J. C. (2021) · Bielefeld University Press eBooks · doi:10.1515/9783839459256 · Cited by 5

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

Ta, H. T., Rahman, A. B. S., Sidorov, G., & Gelbukh, A. F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60887-3_5 · Cited by 5

A state-of-art optimization method for analyzing the tweets of earthquake-prone region

Eligüzel, N., Çetinkaya, C., & Dereli, T. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06109-0 · Cited by 5

STMC: Semantic Tag Medical Concept Using Word2Vec Representation

Soriano, I. M., & Pena, J. L. C. (2018) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2018.00075 · Cited by 5

Analisis de la producción científica basado en las tendencias en temas de investigación. Un estudio de caso sobre inteligencia artificial

Bobadilla, J., Gutiérrez, A., Patricio, M. A., & Bojorque, R. (2019) · Revista española de Documentación Científica · doi:10.3989/redc.2019.1.1583 · Cited by 4

Mining User's Browsing History to Personalize Web Search

Zaveri, V., Dholakia, J., Bandi, I., & Sankhe, S. R. (2018) · International Conference Inventive Communication and Computational Technologies · doi:10.1109/icicct.2018.8473282 · Cited by 4

Analysis of the Word2Vec Model for Semantic Similarities in Indonesian Words

Manalu, L. N. T., Bijaksana, M. A., & Suryani, A. A. (2019) · doi:10.1109/icoict.2019.8835330 · Cited by 4

Text semantic understanding based on knowledge enhancement and multi-granular feature extraction

Tang, X., Hao, B., Dang, X., Zhong, B., Wang, R., & Yan, Z. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326638 · Cited by 4

A relevant document search system model using word2vec approaches

Savittri, S. A., Amalia, A., & Budiman, M. A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1898/1/012008 · Cited by 4

Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach

Sun, K., & Lu, X. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.672243 · Cited by 5

Text Analysis in Consumer Research

Rocklage, M. D., & Rucker, D. D. (2019) · Routledge eBooks · doi:10.4324/9781351137713-21 · Cited by 4

Distant Reading of the Gospel of Thomas and the Gospel of John: Reflection of Methodological Aspects of the Use of Digital Technologies in the Research of Biblical Texts

Špiclová, Z., & Kaše, V. (2020) · Open Theology · doi:10.1515/opth-2020-0111 · Cited by 4

Sprint2Vec: A Deep Characterization of Sprints in Iterative Software Development

Choetkiertikul, M., Banyongrakkul, P., Ragkhitwetsagul, C., Tuarob, S., Dam, H. K., & Sunetnanta, T. T. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3509016 · Cited by 7

Utilizing Sentence Embedding for Dangerous Permissions Detection in Android Apps' Privacy Policies

Baalous, R., & Poet, R. (2021) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.2021010109 · Cited by 3

Makine Öğrenmesi Yöntemleri Kullanılarak Mahkeme Kararlarlarının Kümelenmesi

Görentaş, M. B., & UÇKAN, T. (2023) · Computer Science · doi:10.53070/bbd.1318518 · Cited by 3

The use of associative semantic preprocessor in the interactive dialogue systems in natural language

MIREA, M., & Sachkov, V. E. (2018) · Proceedings of the Institute for System Programming of RAS · doi:10.15514/ispras-2018-30(4)-13 · Cited by 3

Latent Semantic Analysis Boosted Convolutional Neural Networks for Document Classification

Gultepe, E., Kamkarhaghighi, M., & Makrehchi, M. (2018) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc.2018.8697314 · Cited by 3

Gender Detection and Stylistic Differences and Similarities between Males and Females in a Dream Tales Blog

Manna, R., Pascucci, A., & Monti, J. (2019) · ARCHIVIO ISTITUZIONALE DELLA RICERCA-UNIVERSITA DEGLI STUDI DI NAPOLI "LORIENTALE" · Cited by 3

A Semantic Recommender Engine for Idea Generation Improvement

Haiba, M. E., Bassiti, L. E., & Ajhoun, R. (2018) · Computer and Information Science · doi:10.5539/cis.v11n3p112 · Cited by 3

Word Embedding in Small Corpora: A Case Study in Quran

Aghahadi, Z., & Talebpour, A. (2018) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2018.8566605 · Cited by 3

Dreams are more “predictable” than you think

Bertolini, L., Consoli, S., & Weeds, J. (2025) · Frontiers in Sleep · doi:10.3389/frsle.2025.1625185 · Cited by 2

ChiSCor: A Corpus of Freely-Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science

van Dijk, B., van Duijn, M. J., Verberne, S., & Spruit, M. R. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.23 · Cited by 2

Locally Ensconced and Globally Integrated: How Network Cohesion and Range Relate to a Language-Based Model of Organizational Identification

Yang, L., Anshuman, S., Goldberg, A., & Srivastava, S. B. (2025) · American Journal of Sociology · doi:10.1086/735405 · Cited by 2

Analysis of Trends in Online Romanian News Using Semantic Models

Simion, A., Dascălu, M., & Trăușan-Matu, Ș. (2019) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs.2019.00075 · Cited by 2

Sosyal Medya Platformu Üzerinde Gizli Anlam Analizi

Altıntaş, V., Topal, K., & ALBAYRAK, M. (2019) · European Journal of Science and Technology · doi:10.31590/ejosat.590521 · Cited by 2

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

Amin, K., Kapetanakis, S., Polatidis, N., Althoff, K., & Dengel, A. R. (2020) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista49547.2020.9194679 · Cited by 6

Student Perception of Software Engineering Factors Supported by Machine Learning

Marutschke, D. M., White, J. S., & Serdült, U. (2021) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme52200.2021.9591101 · Cited by 2

Fusing contextual word embeddings for concreteness estimation

Incitti, F., & Snidaro, L. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9626843 · Cited by 2

Discursive construction of migrant otherness on Facebook: A distributional semantics approach

Yantseva, V. (2022) · Discourse & Society · doi:10.1177/09579265221117014 · Cited by 2

Locally Ensconced and Globally Integrated: How Positions in Network Structure Relate to a Language-Based Model of Organizational IdentificationPreprint

Yang, L., Goldberg, A., Anshuman, S., & Srivastava, S. B. (2023) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/zrpx7 · Cited by 2

Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering

Schoinas, T., Guinard, B., Esbati, D., & Chalk, R. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006392 · Cited by 1

Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis

Yang, S., & Deravi, F. (2022) · Applied Sciences · doi:10.3390/app12189287 · Cited by 1

Asymmetric Similarity Scores of Color Names in Twitter-Based Language Models

Marutschke, D. M. (2023) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia59471.2023.10335852 · Cited by 1

Getting into bed with embeddings? A comparison of collocations and word embeddings for corpus-assisted discourse analysis

Batchelor, J. (2024) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2024.100117 · Cited by 1

The study of policy coordination: an approach to integrate expert assessment with automated content analysis

Kelchtermans, S., & Santos, A. (2024) · Science and Public Policy · doi:10.1093/scipol/scae074 · Cited by 1

WordPress and Kanban Use in Software Engineering Education Evaluated by Natural Language Processing and Neural Network

Marutschke, D. M., White, J. S., & Serdült, U. (2021) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia52852.2021.9626034 · Cited by 1

Using Neural Architectures to Model Complex Dynamical Systems

Gabriel, N., & Johnson, N. F. (2022) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2022.1124 · Cited by 1

Semantic similarity analysis between the student's reports with the authoritative sources

Segarra-Faggioni, V., Ratté, S., & Jong, F. P. C. M. D. (2022) · International Conference on Education Technology and Computer · doi:10.1145/3572549.3572645 · Cited by 1

A taxonomy of critical incidents in multicultural counseling class

Wu, M., Li, X., & Autin, K. L. (2023) · Counselling Psychology Quarterly · doi:10.1080/09515070.2023.2256273 · Cited by 1

UNLOCKING EDUCATIONAL INSIGHTS: INTEGRATING WORD2VEC EMBEDDINGS AND NAIVE BAYES CLASSIFIER FOR SERIOUS GAME DATA ANALYSIS AND ENHANCEMENT

Mammadli, A. (2023) · Azerbaijan Journal of High Performance Computing · doi:10.32010/26166127.2023.6.2.191.198 · Cited by 1

Cognitive plausibility of count-based versus prediction-based word embeddings: A large-scale N400 study

Dudschig, C., Günther, F., & Mackenzie, I. G. (2025) · Biological Psychology · doi:10.1016/j.biopsycho.2025.109079 · Cited by 2

Detecting meaning in dream reports: An extension of a word search approach.

Bulkeley, K., & Domhoff, G. W. (2010) · Dreaming · doi:10.1037/a0019773 · Cited by 55

Data for: Semi-Supervised Lexicon Generation Using Semantic Relations for Dream Content Analysis

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