superjay1996 / simbiber

SimBiber: A tool for simplifying bibtex with official info.

We often need to simplify the official bib that consists of many information into a shorter version that only maintains necessary information (e.g., author, title, conference/journal name and etc) due to page limitation.

We introduce SimBiber, a simple tool in Python to simplify them automatically. Hope it's helpful for you.

We also highly recommend another wonderful tool for you Rebiber, which is a tool for normalizing bibtex with official info.

Changelog

• 2021.12.31 We build the first version and release it.

Installation

git clone https://github.com/MLNLP-World/Simbiber.git
pip install bibtexparser

Usage（v0.1.0）

python SimBiberParser.py --input_path data/bibtex.bib --output_path out/bibtex.bib --config_path parserConfig.json --if_append_output False --cache_num 100

argument	usage
--input_path	The path to the input bib file that you want to simplify
--output_path	The path to the output bib file that you want to save.
--config_path	The path to the mapper config file
--if_append_output	Whether append simplified data to output bib file.
--cache_num	The number of bib items you want to simplify at once. PLEASE ATTENTION: If you want to simplify a huge bib file, you'd better change it to achieve satisfactory speed.

Example Input and Output

An example simplified output entry with the official information:

@inproceedings{qin-etal-2019-stack,
    title = "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding",
    author = "Qin, Libo  and
      Che, Wanxiang  and
      Li, Yangming  and
      Wen, Haoyang  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1214",
    doi = "10.18653/v1/D19-1214",
    pages = "2078--2087",
    abstract = "Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guiding the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.",
}

An example simplified output entry from the official information:

@inproceedings{qin-etal-2019-stack,
    author = {Qin, Libo  and
     Che, Wanxiang  and
     Li, Yangming  and
     Wen, Haoyang  and
     Liu, Ting},
    booktitle = {Proc. of EMNLP},
    title = {A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding},
    year = {2019}
}

Supported Conferences

The parserConfig.json contains a list of converted json files of the mapper between official full name and simplified name.

Name	Full Name
AAAI	Association for the Advance of Artificial Intelligence
ACL	Association for Computational Linguistics
CCL	Chinese Computational Linguistics
COLING	International Conference on Computational Linguistics
EMNLP	Empirical Methods in Natural Language Processing
ICASSP	International Conference on Acoustics, Speech and Signal Processing
ICLR	International Conference on Learning Representations
ICML	International Conference on Machine Learning
LREC	Language Resources and Evaluation Conference
NeurIPS	Neural Information Processing Systems
NLPCC	Natural Language Processing and Chinese Computing
SemEval	International Workshop on Semantic Evaluation
SIGDIAL	SIGdial Meeting on Discourse and Dialogue

Adding a new conference

You can manually add any conferences from DBLP to config map.

Take ICLR as an example:

• Step 1: Go to DBLP
• Step 2: Find the full name of Conference
• Step 3: Add map to parserConfig.json

{"International Conference on Learning Representations": "ICLR"}

Contact

Please email or [email protected] or [email protected] create Github issues here if you have any questions or suggestions.

Contributor

Thanks to the contributors:

Libo Qin; Qiguang Chen; Qian Liu