--- title: "Who cites A Primer on Neural Network — 102 citing papers" description: "102 papers of the Elamur Research library cite «A Primer on Neural Network Models for Natural Language Processing», found in their…" canonical: "https://research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Primer on Neural Network Models for Natural…](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/) / cited by # Who cites «A Primer on Neural Network Models for Natural Language Processing» 102 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «A Primer on Neural Network Models for Natural Language Processing»](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/) 1. [**Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations**Eliyahu Kiperwasser, Yoav Goldberg*2016 · 909 citations*](https://md.research.elamur.ai/free/en/papers/kiperwasser-2016-simple-accurate-dependency-parsing-bidirectional/) 2. **[Deep multi-task learning with low level tasks supervised at lower layers](https://md.research.elamur.ai/free/en/papers/sogaard-2016-deep-multi-task-learning-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anders Søgaard, Yoav Goldberg*2016 · 586 citations* 3. **[A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/zhang-2017-sensitivity-analysis-practitioners-guide-convolutional/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Ye Zhang, Byron Wallace*2017 · 260 citations* 4. **[Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints](https://md.research.elamur.ai/free/en/papers/mrksic-2017-semantic-specialization-distributional-word-vector/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, et al.*2017 · 236 citations* 5. **[Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://md.research.elamur.ai/free/en/papers/plank-2016-multilingual-part-speech-tagging-bidirectional/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Barbara Plank, Anders Søgaard, Yoav Goldberg*2016 · 203 citations* 6. [**Noise-Robust Morphological Disambiguation for Dialectal Arabic**Nasser Zalmout, Alexander Erdmann, Nizar Habash*2018 · 43 citations*](https://md.research.elamur.ai/free/en/papers/zalmout-2018-noise-robust-morphological-disambiguation-dialectal/) 7. [**Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew**Adam Amram, Anat Ben David, Reut Tsarfaty*2018 · 40 citations*](https://md.research.elamur.ai/free/en/papers/amram-2018-representations-architectures-neural-sentiment-analysis/) 8. **[Complementary Strategies for Low Resourced Morphological Modeling](https://md.research.elamur.ai/free/en/papers/erdmann-2018-complementary-strategies-low-resourced-morphological/) [Workshop on Computational Research in Phonetics, Phonology, and Morphology](https://md.research.elamur.ai/free/en/venues/workshop-on-computational-research-in-phonetics-phonology-and-morphology/)**Alexander Erdmann, Nizar Habash*2018 · 18 citations* 9. **[Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations](https://md.research.elamur.ai/free/en/papers/ribeiro-2018-deep-dialog-act-recognition-using/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Eugenio Ribeiro; Ricardo Ribeiro;, David Martins de Matos*2018 · 18 citations* 10. [**Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages arXiv:2111.13974**Somnath Banerjee, Maulindu Sarkar, Nancy Agrawal, et al.*2021 · 17 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2021-exploring-transformer-based-models-identify/) 11. **[Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings](https://md.research.elamur.ai/free/en/papers/guzman-2016-machine-translation-evaluation-arabic-morphologically/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Francisco Guzmán, Houda Bouamor, Ramy Baly, et al.*2016 · 12 citations* 12. **[A Study on Dialog Act Recognition using Character-Level Tokenization](https://md.research.elamur.ai/free/en/papers/ribeiro-2018-study-dialog-act-recognition-using/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Eugenio Ribeiro; Ricardo Ribeiro;, David Martins de Matos*2018 · 5 citations* 13. **[Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets](https://md.research.elamur.ai/free/en/papers/meisheri-2017-textmining-emoint-2017-deep-learning/) [Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis](https://md.research.elamur.ai/free/en/venues/wassa/)**Hardik Meisheri, Rupsa Saha, Priyanka Sinha, et al.*2017 · 5 citations* 89 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/) — A Primer on Neural Network Models for Natural Language Processing