The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Learning Text Similarity with Siamese Recurrent Networks
Open accessAbstract
This paper presents a deep architecture for learning a similarity metric on variablelength character sequences.The model combines a stack of character-level bidirectional LSTM's with a Siamese architecture.It learns to project variablelength strings into a fixed-dimensional embedding space by using only information about the similarity between pairs of strings.This model is applied to the task of job title normalization based on a manually annotated taxonomy.A small data set is incrementally expanded and augmented with new sources of variance.The model learns a representation that is selective to differences in the input that reflect semantic differences (e.g., "Java developer" vs. "HR manager") but also invariant to nonsemantic string differences (e.g., "Java developer" vs. "Java programmer").
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“Siamese networks are widely applied in nonlinear metric learning across domains such as computer vision and speech recognition.”
From Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches (2025) · cites this paper as [47]
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 2×
“In this equation and below the logistic function is denoted by σ The Long Short-Term Memory (Hochreiter and Schmidhuber, 1997) variant of RNNs in particular has had success in tasks related to natural language processing, such as text classification (Graves, 2012) and language translation (Sutskever…”
MethodAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“The parameters of the model are optimized using the Adam method (Kingma and Ba, 2014) and each model is trained until convergence.”
MethodGlove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · cited 2×
“, 2013) and Glove (Pennington et al., 2014) have seen much use in such models, but some go beyond the word level and represent text as a sequence of characters (Kim et”
Background(2014) · Cited by 33,419
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · cited 2×
“In the last few years, neural network models have been applied to tasks ranging from machine translation (Zou et al., 2013; Cho et al., 2014) to question answering (Weston et al.”
MethodDistributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · cited 3×
“For example, word similarity models based on dense embeddings (Mikolov et al., 2013) have recently been applied in diverse settings, such as sentiment analysis (dos Santos and Gatti, 2014) and recommender systems (Barkan and Koenig-stein, 2016).”
BackgroundMethodReducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · cited 1×
“…and below the logistic function is denoted by σ The Long Short-Term Memory (Hochreiter and Schmidhuber, 1997) variant of RNNs in particular has had success in tasks related to natural language processing, such as text classification (Graves, 2012) and language translation (Sutskever et al., 2014).”
BackgroundBidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · cited 1×
“(1) Bidirectional RNNs (Schuster and Paliwal, 1997) incorporate both future and past context by running the reverse of the input through a separate RNN.”
BackgroundNatural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923 · cited 1×
“The use of (deep) neural networks for NLP has recently received much attention, starting from the seminal papers employing convolutional networks on traditional NLP tasks (Collobert et al., 2011) and the availability of high quality semantic word representations (Mikolov et al., 2013).”
Method
Research works printed but not linked: 5
- Jane Bromley, James W Bentz, Léon Bottou, Is- abelle Guyon, Yann LeCun, Cliff Moore, Eduard Säckinger, and Roopak Shah. 1993. Signature ver- ification using a "siamese" time delay neural net- work. International Journal of Pattern Recognition and Artificial Intelligence, 7(04):669-688.
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Cor- rado, and Jeff Dean. 2013. Distributed representa- tions of words and phrases and their compositional- ity. In Advances in neural information processing systems, pages 3111-3119.
- Mike Schuster and Kuldip K Paliwal. 1997. Bidirec- tional recurrent neural networks. Signal Processing, IEEE Transactions on, 45(11):2673-2681.
- Martijn Spitters, Remko Bonnema, Mihai Rotaru, and Jakub Zavrel. 2010. Bootstrapping information ex- traction mappings by similarity-based reuse of tax- onomies. In CEUR Workshop Proceedings, volume 673.
- Longqi Yang, Yin Cui, Fan Zhang, John P Pollak, Serge Belongie, and Deborah Estrin. 2015. Plate- click: Bootstrapping food preferences through an adaptive visual interface. In Proceedings of the 24th ACM International on Conference on Informa- tion and Knowledge Management, pages 183-192. ACM.
Cited by
Sorted by citations
Deep Learning--based Text Classification
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad-Khasmakhi, N., Asgari-Chenaghlu, M., & Gao, J. (2021) · ACM Computing Surveys · doi:10.1145/3439726 · Cited by 2,122
“[120] present a similar model that uses character-level Bi-LSTMs for f1 and f2, and the”
MethodDeep Learning for Entity Matching
Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., Deep, R., Arcaute, E., & Raghavendra, V. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196926 · Cited by 616
“Table 13: Comparing the F1 accuracy of the Hybrid and RNN models in our submission with the model proposed in [56] for an NLP task.”
BackgroundMethodResultInfluentialDeep Learning Based Text Classification: A Comprehensive ReviewPreprint
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Asgari-Chenaghlu, M., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03705 · Cited by 385
“[120] present a similar model that uses character-level Bi-LSTMs for f1 and f2, and the”
MethodFake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S., & On, B.-W. (2020) · IEEE Access · doi:10.1109/access.2020.3019735 · Cited by 350
“Deep learning models such as recurrent neural networks (RNN) and its variants [20]–[22] and convolution neural networks (CNN) [23] have been used effectively in many NLP tasks that share similarities to fake news and consist of calculating semantic similarity between sentences [24], [25] and…”
BackgroundSampling-bias-corrected neural modeling for large corpus item recommendations
Yi, X., Yang, J. H., Hong, L., Cheng, D. Z., Heldt, L., Kumthekar, A., Zhao, Z., Li, W., & Ed, H. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3346996 · Cited by 267
“Building neural networks with two towers has recently become a popular approach in several natural language tasks including modeling sentence similarities [31], response suggestions [24], and text-based information retrieval [17, 37].”
Background
Metadata
- Authors
- Neculoiu, P., Versteegh, M., & Rotaru, M.
- ORCID
- Versteegh, Maarten
- Venue
- Proceedings of the 1st Workshop on Representation Learning for NLP
- Published
- 2016
- Pages
- 148-157
- OpenAlex
- W2510940142
- Cited by
- 341
- References
- 51
- Citations by year
- 2026: 6 · 2025: 21 · 2024: 30 · 2023: 31 · 2022: 43 · 2021: 63 · 2020: 64 · 2019: 37 · 2018: 36 · 2017: 9
- FWCI
- 14.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Representation Learning for NLP (RepL4NLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Keywords
- text similarity, Siamese network, bidirectional LSTM, character embeddings, similarity metric learning, taxonomy (general classification scheme), data augmentation, semantic representation, string embeddings
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