Singapore

EMNLP 2023

ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution

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coreference resolution

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workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

An Open-source Web-based Application for Development of Resources and Technologies in Underresourced Languages

AWARE-TEXT: An Android Package for Mobile Phone Based Text Collection and On-Device Processing | VIDEO

Beyond the Repo: A Case Study on Open Source Integration with GECToR

calamanCy: A Tagalog Natural Language Processing Toolkit

Deepparse : A State-Of-The-Art Library for Parsing Multinational Street Addresses

EDGAR-CRAWLER: Finding Needles in the Haystack of Financial Documents

Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models | VIDEO

Kani: A Lightweight and Highly Hackable Framework for Language Model Applications | VIDEO

nanoT5: Fast & Simple Pre-training and Fine-tuning of T5 Models with Limited Resources

PyThaiNLP: Thai Natural Language Processing in Python

https://d3smihljt9218e.cloudfront.net/transcript/59308/subtitle/25351bd7e9517a6e4a0f323fc0c3e8b9.vtt

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ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution

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