To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words.
WangL., LiY., HuangJ., et al., Learning two-branch neural networks for image-text matching tasks, IEEE Trans Pattern Anal Mach Intell41(2) (2019), 394–407.
2.
YangY., WangL., XieD., et al., Multi-sentence auxiliary adversarial networks for fine-grained text-to-image synthesis, IEEE Trans Image Process30 (2021), 2798–2809.
3.
ZhangY., ZhouW., WangM., et al., Deep relation embedding for cross-modal retrieval, IEEE Trans Image Process30 (2021), 617–627.
4.
CanalesL., StrapparavaC., BoldriniE., et al., Intensional learning to efficiently build up automatically annotated emotion corpora, IEEE Trans Affect Comput11(2) (2020), 335–347.
5.
LuoY., ZhangX., QinY., et al., Tourism attraction selection with sentiment analysis of online reviews based on probabilistic linguistic term sets and the IDOCRIW-COCOSO model, Int J Fuzzy Syst23 (2021), 295–308.
6.
LiX., ZhouF., XuC., et al., SEA: Sentence encoder assembly for video retrieval by textual queries, IEEE Trans Multim23 (2021), 4351–4362.
7.
KhamenehA., KilicmanA., AliF., Transitive fuzzy similarity multigraph-based model for alternative clustering in multi-criteria group decision-making problems, Int J Fuzzy Syst 2022.
8.
BenH., PanY., LiY., et al., Unpaired image captioning with semantic-constrained self-learning, IEEE Trans Multim 2021, 1–1.
9.
GaoY., XuY., HuangH., et al., Jointly learning topics in sentence embedding for document summarization, IEEE Trans Knowl Data Eng32(4) (2020), 688–699.
10.
HuangY., WangJ., WangL., Few-shot image and sentence matching via aligned cross-modal memory, IEEE Trans Pattern Anal Mach Intell 2021, 1–1.
11.
WuZ., KangJ., JiangQ., Semantic key generation based on natural language, Int J Intell Syst 2021, 1–24.
12.
JohnW., MohitB., KevinG., et al., Towards universal paraphrastic sentence embeddings, in Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, 2016.
13.
TomK., AlexeyB., Maarten deR., Siamese CBOW: Optimizing word embeddings for sentence representations, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 2016.
14.
WangS., ZhangJ., ZongC., Learning sentence representation with guidance of human attention, in Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, 2017, 4137–4143.
15.
QuanZ., WangZ., LeY., et al., An efficient framework for sentence similarity modeling, IEEE ACM Trans Audio Speech Lang Process27(4) (2019), 853–865.
16.
WangB., JayC., Kuo, SBERT-WK: A sentence embedding method by dissecting BERT-based word models, IEEE ACM Trans Audio Speech Lang Process28 (2020), 2146–2157.
17.
DimitrisG., ShahramD., MassimoM., et al., Quantum cognitively motivated decision fusion for video sentiment analysis, in Proceedings of the 35th Conference on Artificial Intelligence, Online, 2021, 827–835.
18.
WangB., ZhaoD., ChristinaL., et al., Encoding word order in complex embeddings, in Proceedings of the 9th International Conference on Learning Representations (ICLR2020), Addis Ababa, 2020, 1–15.
19.
ZhangP., NiuJ., SuZ., et al., End-to-end quantum-like language models with application to question answering, in Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), Louisiana, 2018, 5666–5673.
20.
DevlinJ., ChangM., LeeK., 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, NAACL-HLT 2019, 2019, 4171–4186.
21.
LiB., ZhouH., HeJ., et al., On the sentence embeddings from pre-trained language models, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), Online, 2020, 9119–9130.
22.
YanY., LiR., WangS., ConSERT: A contrastive framework for self-supervised sentence representation transfer, in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021, 5065–5075.
23.
GaoT., YaoX., ChenD., SimCSE: Simple contrastive learning of sentence embeddings, in Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2021), Online, 2021, 6894–6910.
24.
JiangY., WangW., Deep continuous prompt for contrastive learning of sentence embeddings, 2022, arXiv:2203.06875v1.
25.
LiuF., JiaoY., MassiahJ., et al., Trans-encoder: unsupervised sentence-pair modelling through self- and mutual-distillations, in Proceedings of the 11th International Conference on Learning Representations (ICLR 2022), online, 2022, arXiv:2109.13059v4.
26.
LanZ., ChenM., GoodmanS., et al., ALBERT: A lite BERT for self-supervised learning of language representations, in Proceedings of the 8th International Conference on Learning Representations, ICLR 2020, 2020.
27.
AlexisC., GuillaumeL., Cross-lingual language model pretraining, in Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, Neur IPS 2019, 2019, 7057–7067.
28.
LiuY., OttM., GoyalN., et al., RoBERTa: A robustly optimized BERT pretraining approach, CoRR, abs/1907.11692, 2019. http://arxiv.org/abs/1907.11692
29.
SuJ., DaiQ., GuerinF., et al., BERT-hLSTMs: BERT and hierarchical LSTMs for visual story telling, Comput Speech Lang67 (2021), 101169.
30.
AraseY., TsujiiJ., Transfer fine-tuning of BERT with phrasal paraphrases, Comput Speech Lang66 (2021), 101164.
31.
BrownT., MannB., RyderN., et al., Language models are few-shot learners, in Proceedings of Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 2020.
32.
ZhaoR., MaoK., Fuzzy bag-of-words model for document representation, (2), IEEE Trans Fuzzy Syst26 (2018), 794–804.
33.
KarpagamA., ManikandanM., Multi-level fuzzy based Renyi entropy for linguistic classification of texts in natural scene images, Int J Fuzzy Syst22 (2020), 438–449.
34.
QiuD., ZhengQ., Improving Text Rank algorithm for automatic keyword extraction with tolerance rough set, Int J Fuzzy Syst 2021.
35.
VashishthaS., SusanS., Sentiment cognition fromwords shortlisted by fuzzy entropy, IEEE Trans Auton Ment Dev12(3) (2020), 541–550.
36.
CardoneB., Di MartinoF. and SenatoreS., Improving the emotion-based classification by exploiting the fuzzy entropy in FCM clustering, Int J Intell Syst36(11) (2021), 6944–6967.
37.
MehlawatM., GuptaP., KhaitanA., Multiobjective fuzzy vehicle routing using Twitter data: reimagining the delivery of essential goods, Int J Intell Syst36(7) (2021), 3566–3595.
38.
OrouskhaniM., ShiD., ChengX., A fuzzy adaptive dynamic NSGA-II with fuzzy-based borda ranking method and its application to multimedia data analysis, IEEE Trans Fuzzy Syst29(1) (2021), 118–128.
39.
DangE., LukR., AllanJ., A principled approach using fuzzy set theory for passage-based document retrieval, IEEE Trans Fuzzy Syst29(7) (2021), 1967–1977.
40.
ChangT., LeeC., Subtopic segmentation for small corpus using a novel fuzzy model, IEEE Trans Fuzzy Syst15(4) (2007), 699–710.
41.
ValerieC., ValeriaM., KeeleyA., et al., Ontological and fuzzy set similarity between perception-based words, in Proceedings of 2019 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2019, 2019, 1–6.
42.
ZhaoF., ZhuZ., HanP., A novel model for semantic similarity measurement based on wordnet and word embedding, J Intell Fuzzy Syst40(5) (2021), 9831–9842.
43.
PascualJ., FernandoS., Implementing word Net measures of lexical semantic similarity in a fuzzy logic programming system, Theory Pract Log Program21(2) (2021), 264–282.
44.
DasM., GhoshS., Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach, IEEE Trans Emerg Top Comput Intell5(3) (2021), 361–372.
45.
BelkhatirM., Fuzzy-logic-based integration of web contextual linguistic structures for enriching conceptual visual representations, IEEE Trans Emerg Top Comput Intell3(4) (2019), 351–356.
46.
LiZ., ZhangP., GeX., et al., Uncertainty measurement for a fuzzy relation information system, IEEE Trans Fuzzy Syst27(12) (2019), 2338–2352.
47.
LiQ., LiL., LiQ., et al., A comprehensive exploration on spider with fuzzy decision text-to-SQL model, IEEE Trans Ind Informatics16(4) (2020), 2542–2550.
48.
YangM., LiuS., ChenK., et al., A hierarchical clustering approach to fuzzy semantic representation of rare words in neural machine translation, IEEE Trans Fuzzy Syst28(5) (2020), 992–1002.
49.
LiuQ., HuangH., XuanJ., et al., A fuzzy word similarity measure for selecting top-k similar words in query expansion, IEEE Trans Fuzzy Syst 2020, 1–1.
50.
CrossV., MokrenkoV., CrockettK., et al., Using fuzzy set similarity in sentence similarity measures, in Proceeedings of the 29th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2020, 2020, 1–8.
DiederikA., JonitoA., LesterB., et al., Context and interference effects in the combinations of natural concepts, in Proceedings of Modeling and Using Context-10th International and Interdisciplinary Conference10257 (2017), 677–690.
53.
AertsD., BeltranL., Quantum structure in cognition: human language as a Boson gas of entangled words, Found Sci25 (2020), 755–802.
54.
AertsD., BeltranL., GerienteS., et al., Quantum-theoretic modeling in computer science: a complex Hilbert space model for entangled concepts in corpuses of documents, Int J Theor Phys99 (2019), 1–15.
55.
HeR., WangY., SongD., et al., A dependency parser for spontaneous Chinese spoken language, ACM Trans Asian Low Resour Lang Inf Process17(4) (2018), 28:1–28.
56.
DiederikA., AertsA., LesterB., et al., Towards a quantum world wide web, Theor Comput Sci752 (2018), 116–131.
57.
TrevorC., DominicW., Bringing order to neural word embeddings with embeddings augmented by random permutations (EARP), in Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL2018), Brussels, 2018, 465–475.
58.
LiuY., ZhangY., LiQ., et al., What does your smile mean? Jointly detecting multi-modal sarcasm and sentiment using quantum probability, in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP2021), Online, 2021, 871–880.
59.
JiangY., ZhangP., GaoH., et al., A quantum interference inspired neural matching model for Ad-hoc retrieval, in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020), Online, 2020, 19–28.
ZhangY., SongD., LiX., et al., A quantum-like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis, Inform Fusion62 (2020), 14–31.
62.
ZhangY., SongD., ZhangP., et al., A quantum-inspired sentiment representation model for twitter sentiment analysis, Appl Intell49(8) (2019), 3093–3108.
63.
ZhangY., SongD., ZhangP., et al., A quantum-inspired multimodal sentiment analysis framework, Theor Comput Sci752 (2018), 21–40.
64.
DimitrisG., LiQ., YuY., et al., An entanglement-driven fusion neural network for video sentiment analysis, in Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Online, 2021, 1736–1742.
65.
ZhangY., LiuY., LiQ., et al., CFN: A complex-valued fuzzy network for sarcasm detection in conversations, IEEE Trans Fuzzy Syst29(12) (2021), 3696–3710.
66.
ZhangL., ZhangP., MaX., et al., A generalized language model in tensor space, in Proceedings of the 33rd Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, and the 9th Symposium on Educational Advances in Artificial Intelligence, Hawaii, 2019, 7450–7458.
67.
MaX., ZhangP., ZhangS., et al., A tensorized transformer for language modeling, in Proceedings of the Annual Conference on Neural Information Processing Systems 2019, Vancouver, 2019, 2229–2239.
68.
ZhangS., ZhangP., MaX., et al., TensorCoder: Dimension-wise attention via tensor representation for natural language modeling, in Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Online, 2020, 1–11.
69.
JiangY., ZhangP., GaoH., et al., A match-transformer framework for modeling diverse relevance patterns in Ad-hoc retrieval, Aust J Intell Inf Process Syst17(1) (2019), 39–47.
