Citation: | Chenchen ZHANG, Qiuchi LI, Zhan SU, et al., “Word2State: Modeling Word Representations as States with Density Matrices,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–12, xxxx doi: 10.23919/cje.2023.00.336 |
[1] |
R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. ACM Press, New York, NY, USA, 1999.
|
[2] |
Y. Bengio, R. Ducharme, P. Vincent, et al, “A neural probabilistic language model,” The Journal of Machine Learning Research, vol. 3, pp. 1137–1155, 2003
|
[3] |
P. Zhang, J. B. Niu, Z. Su, et al, “End-to-end quantum-like language models with application to question answering,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, pp. 5666–5673, 2018.
|
[4] |
Q. C. Li, B. Y. Wang, and M. Melucci, “CNM: An interpretable complex-valued network for matching,” in Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, pp. 4139–4148, 2019.
|
[5] |
Y. W. Chen, Y. Pan, and D. Y. Dong, “Quantum language model with entanglement embedding for question answering,” IEEE Transactions on Cybernetics, vol. 53, no. 6, pp. 3467–3478, 2023 doi: 10.1109/TCYB.2021.3131252
|
[6] |
P. Zhang, W. J. Hui, B. Y. Wang, et al, “Complex-valued neural network-based quantum language models,” ACM Transactions on Information Systems, vol. 40, no. 4, article no. article no. 1, 2022 doi: 10.1145/3505138
|
[7] |
Q. C. Li, S. Uprety, B. Y. Wang, et al, “Quantum-inspired complex word embedding,” in Proceedings of the Third Workshop on Representation Learning for NLP, Melbourne, Australia, pp. 50–57, 2018.
|
[8] |
B. Y. Wang, Q. C. Li, M. Melucci, et al, “Semantic hilbert space for text representation learning,” in Proceedings of World Wide Web Conference, San Francisco, CA, USA, pp. 3293–3299, 2019.
|
[9] |
B. Y. Wang, D. H. Zhao, C. Lioma, et al, “Encoding word order in complex embeddings,” in Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, pp. 1–15, 2020.
|
[10] |
J. R. Busemeyer and P. D. Bruza, Quantum Models of Cognition and Decision, Cambridge University Press, New York, NY, USA, 2012.
|
[11] |
A. Mnih and G. Hinton, “Three new graphical models for statistical language modelling,” in Proceedings of the 24th International Conference on Machine Learning, Corvalis, OR, USA, pp. 641–648, 2007.
|
[12] |
A. Mnih and G. Hinton, “A scalable hierarchical distributed language model,” in Proceedings of the 21st International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp. 1081–1088, 2008.
|
[13] |
R. Collobert, J. Weston, L. Bottou, et al, “Natural language processing (almost) from scratch,” The Journal of Machine Learning Research, vol. 12, pp. 2493–2537, 2011
|
[14] |
T. Mikolov, M. Karafiát, L. Burget, et al, “Recurrent neural network based language model,” in Proceedings of the 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, pp. 1045–1048, 2010.
|
[15] |
T. Mikolov, I. Sutskever, K. Chen, et al, “Distributed representations of words and phrases and their compositionality,” in Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, pp. 3111–3119, 2013.
|
[16] |
Z. S. Harris, “Distributional structure,” Word, vol. 10, no. 2–3, pp. 146–162, 1954 doi: 10.1080/00437956.1954.11659520
|
[17] |
J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1532–1543, 2014.
|
[18] |
A. Radford, J. Wu, R. Child, et al, “Language models are unsupervised multitask learners,” OpenAI, vol. 1, no. 8, article no. 9, 2019
|
[19] |
J. Devlin, M. W. Chang, K. Lee, et al, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, pp. 4171–4186, 2019.
|
[20] |
C. J. van Rijsbergen, The Geometry of Information Retrieval. Cambridge University Press, New York, NY, USA, 2004.
|
[21] |
A. Sordoni, J. Y. Nie, and Y. Bengio, “Modeling term dependencies with quantum language models for IR,” in Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 653–662, 2013.
|
[22] |
P. Zhang, Z. Su, L. P. Zhang, et al, “A quantum many-body wave function inspired language modeling approach,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, pp. 1303–1312, 2018.
|
[23] |
Y. C. Liu, Q. C. Li, B. Y. Wang, et al, “A survey of quantum-cognitively inspired sentiment analysis models,” ACM Computing Surveys, vol. 56, no. 1, article no. article no., 2023
|
[24] |
S. Merity, C. M. Xiong, J. Bradbury, et al, “Pointer sentinel mixture models,” in Proceedings of the 5th International Conference on Learning Representations, Toulon, France, pp. 1–15, 2017.
|
[25] |
G. A. Miller and W. G. Charles, “Contextual correlates of semantic similarity,” Language and Cognitive Processes, vol. 6, no. 1, pp. 1–28, 1991 doi: 10.1080/01690969108406936
|
[26] |
E. Bruni, N. K. Tran, and M. Baroni, “Multimodal distributional semantics,” Journal of Artificial Intelligence Research, vol. 49, no. 1, pp. 1–47, 2014 doi: 10.1613/jair.4135
|
[27] |
K. Radinsky, E. Agichtein, E. Gabrilovich, et al, “A word at a time: Computing word relatedness using temporal semantic analysis,” in Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India, pp. 337–346, 2011.
|
[28] |
G. Halawi, G. Dror, E. Gabrilovich, et al, “Large-scale learning of word relatedness with constraints,” in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 1406–1414, 2012.
|
[29] |
H. Rubenstein and J. B. Goodenough, “Contextual correlates of synonymy,” Communications of the ACM, vol. 8, no. 10, pp. 627–633, 1965 doi: 10.1145/365628.365657
|
[30] |
T. Luong, R. Socher, and C. D. Manning, “Better word representations with recursive neural networks for morphology,” in Proceedings of the 17th Conference on Computational Natural Language Learning, Sofia, Bulgaria, pp. 104–113, 2013.
|
[31] |
F. Hill, R. Reichart, and A. Korhonen, “Simlex-999: Evaluating semantic models with (Genuine) similarity estimation,” Computational Linguistics, vol. 41, no. 4, pp. 665–695, 2015 doi: 10.1162/COLI_a_00237
|
[32] |
D. Gerz, I. Vulić, F. Hill, et al, “SimVerb-3500: A large-scale evaluation set of verb similarity,” in Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA, pp. 2173–2182, 2016.
|
[33] |
L. Finkelstein, E. Gabrilovich, Y. Matias, et al, “Placing search in context: The concept revisited,” in Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China, pp. 406–414, 2001.
|
[34] |
C. Spearman, “The proof and measurement of association between two things,” The American Journal of Psychology, vol. 15, no. 1, pp. 72–101, 1904 doi: 10.2307/1412159
|
[35] |
F. Jelinek, R. L. Mercer, L. R. Bahl, et al, “Perplexity—a measure of the difficulty of speech recognition tasks,” The Journal of the Acoustical Society of America, vol. 62, no. S1, article no. article no. S63, 1977 doi: 10.1121/1.2016299
|
[36] |
B. Pang and L. Lee, “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,” in Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Ann Arbor, MI, USA, pp. 115–124, 2005.
|
[37] |
M. Q. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp. 168–177, 2004.
|
[38] |
B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, pp. 271–278, 2004.
|
[39] |
J. Wiebe, T. Wilson, and C. Cardie, “Annotating expressions of opinions and emotions in language,” Language Resources and Evaluation, vol. 39, no. 2–3, pp. 165–210, 2005 doi: 10.1007/s10579-005-7880-9
|
[40] |
R. Socher, A. Perelygin, J. Wu, et al, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA, pp. 1631–1642, 2013.
|
[41] |
W. B. Dolan and C. Brockett, “Automatically constructing a corpus of sentential paraphrases,” in Proceedings of the Third International Workshop on Paraphrasing, Jeju Island, Korea, pp. 9–16, 2005.
|
[42] |
A. Joulin, E. Grave, P. Bojanowski, et al, “Bag of tricks for efficient text classification,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp. 427–431, 2017.
|
[43] |
M. Pagliardini, P. Gupta, and M. Jaggi, “Unsupervised learning of sentence embeddings using compositional n-gram features,” in Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA, pp. 528–540, 2018.
|
[44] |
F. Hill, K. Cho, and A. Korhonen, “Learning distributed representations of sentences from unlabelled data,” in Proceedings of 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA, pp. 1367–1377, 2016.
|
[45] |
F. Hill, K. Cho, A. Korhonen, et al, “Learning to understand phrases by embedding the dictionary,” Transactions of the Association for Computational Linguistics, vol. 4, pp. 17–30, 2016 doi: 10.1162/tacl_a_00080
|
[46] |
Y. H. Chen, “Convolutional neural network for sentence classification,” Master Thesis, University of Waterloo, Waterloo, Ontario, Canada, 2015.
|
[47] |
A. Conneau, D. Kiela, H. Schwenk, et al, “Supervised learning of universal sentence representations from natural language inference data,” in Proceedings of 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 670–680, 2017.
|
[48] |
T. B. Brown, B. Mann, N. Ryder, et al, “Language models are few-shot learners,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, article no. 159, 2020.
|