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papers on legal ai

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## Must-read papers on LegalAI.

Contributed by [Cunchao Tu](http://thunlp.org/~tcc/).

### Highly relevant papers

1. **Learning to Predict Charges for Criminal Cases with Legal Basis.**
*Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao* EMNLP 2017. [paper](https://arxiv.org/pdf/1707.09168.pdf)

### Similar case matching (Semantic mathcing)

1. **Convolutional neural network architectures for matching natural language sentences.**
*Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen* NIPS 2014. [paper](http://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences.pdf)

1. **ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs.**
*Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bowen Zhou* TACL 2016. [paper](http://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences.pdf)

1. **Reasoning about Entailment with Neural Attention.**
*Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom* ICLR 2016. [paper](https://arxiv.org/pdf/1509.06664.pdf)

1. **Convolutional Neural Network for Paraphrase Identification.**
*Wenpeng Yin, Hinrich Schütze* NAACL 2015. [paper](https://aclanthology.info/pdf/N/N15/N15-1091.pdf)

1. **Short Text Similarity with Word Embeddings.**
*Tom Kenter, Maarten de Rijke* CIKM 2015. [paper](https://pdfs.semanticscholar.org/91a9/29b7e445629132605e73ad3610b258c44888.pdf)

1. **Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks.**
*Hua He, Kevin Gimpel, Jimmy Lin* EMNLP 2015. [paper](https://pdfs.semanticscholar.org/0f69/24633c56832b91836b69aedfd024681e427c.pdf)

1. **Deep Fusion LSTMs for Text Semantic Matching.**
*Pengfei Liu, Xipeng Qiu, Jifan Chen, Xuanjing Huang* ACL 2016. [paper](https://pdfs.semanticscholar.org/adc1/84fcb04107f95e35ea1b07ef9aad749da8d7.pdf)

1. **A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations.**
*Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xueqi Cheng* AAAI 2016. [paper](https://pdfs.semanticscholar.org/b44f/f5104557fc9b900d48f02605dd42d794927d.pdf)

### Graph summarization

1. **Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps.**
*Tobias Falke, Iryna Gurevych* EMNLP 2017. [paper](http://aclweb.org/anthology//D/D17/D17-1322.pdf)

1. **A PropositionBased Abstractive Summariser.**
*Yimai Fang, Haoyue Zhu, Ewa Muszynska, Alexander Kuhnle, Simone Teufel* COLING 2016. [paper](https://pdfs.semanticscholar.org/4478/5557136ff2de081ec9008b66a787ddc8414a.pdf)

1. **Abstractive Multi-document Summarization with Semantic Information Extraction.**
*Wei Li* EMNLP 2015. [paper](http://ai2-s2-pdfs.s3.amazonaws.com/4f87/81df9fd1d7473a28b86885af30a1cad2a2d0.pdf)

1. **Abstractive News Summarization based on Event Semantic Link Network.**
*Wei Li, Lei He, Hai Zhuge* COLING 2016. [paper](https://pdfs.semanticscholar.org/b41e/774fc2a41982a94d41d4f6c1c75658c2cefa.pdf)

1. **Toward Abstractive Summarization Using Semantic Representations.**
*Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith* NAACL 2015. [paper](http://www.aclweb.org/anthology/N15-1114)

### Datasets

**Case documents:** \approx 20,000,000 cases. Among them, \approx 3,000,000 documents contain detailed annotations, including courts, types, causes, relevant laws and dates.

**Laws:** 1617 laws, including penal law, civil law and so on.

### AI and Laws

**ICAIL**(International Conference on Artificial Intelligence and Law)  is the primary international conference addressing research in Artificial Intelligence and Law, and has been organized biennially since 1987 under the auspices of the International Association for Artificial Intelligence and Law (IAAIL).

**Journal:** [Artificial Intelligence and Law](https://link.springer.com/journal/volumesAndIssues/10506)

**Proceedings:**

1. [ICAIL 2017](https://nms.kcl.ac.uk/icail2017/papers.php)

1. [ICAIL 2015](https://dl.acm.org/citation.cfm?id=2746090)

### Projects for novices

1. **Charge Prediction:** employ CNN and LSTM models to predict charges on civil cases.

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