no code implementations • SemEval (NAACL) 2022 • Aapo Pietiläinen, Shaoxiong Ji
The shared task is about multilingual complex named entity recognition.
no code implementations • 7 Apr 2024 • Shaoxiong Ji, Pinzhen Chen
Fine-tuning large language models for multilingual downstream tasks requires a diverse set of languages to capture the nuances and structures of different linguistic contexts effectively.
no code implementations • 25 Mar 2024 • Shaoxiong Ji, Timothee Mickus, Vincent Segonne, Jörg Tiedemann
We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability -- which we argue is of use for machine translation but detrimental elsewhere.
no code implementations • 20 Mar 2024 • Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer Van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive.
1 code implementation • 12 Mar 2024 • Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona de Gibert, Shaoxiong Ji, Niki Andreas Lopi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled.
no code implementations • 24 Jan 2024 • Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann, André F. T. Martins, Hinrich Schütze
Large language models (LLMs) have advanced the state of the art in natural language processing.
no code implementations • 19 Nov 2023 • Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.
1 code implementation • 16 Sep 2023 • Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev, Andrey Kutuzov, Barry Haddow, Kenneth Heafield
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
no code implementations • 12 Sep 2023 • Shaoxiong Ji, Wei Sun, Pekka Marttinen
We consider two interesting research questions: 1) how is information distributed over long documents, and 2) how does content reduction, such as token selection and text summarization, affect the information density in long documents.
1 code implementation • 9 Aug 2023 • Kailai Yang, Tianlin Zhang, Shaoxiong Ji, Sophia Ananiadou
However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources.
no code implementations • 20 Apr 2023 • Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria, Jörg Tiedemann
In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.
no code implementations • 19 Apr 2023 • Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Sophia Ananiadou
In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion.
2 code implementations • 6 Apr 2023 • Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie, Ziyan Kuang, Sophia Ananiadou
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
no code implementations • 25 Jan 2023 • Shaoxiong Ji, Ya Gao, Pekka Marttinen
Adverse drug events (ADEs) are an important aspect of drug safety.
no code implementations • 25 Aug 2022 • Ming Jiang, Shaoxiong Ji
Multimodal sentiment analysis is an important research task to predict the sentiment score based on the different modality data from a specific opinion video.
1 code implementation • 21 Mar 2022 • Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.
no code implementations • 8 Jan 2022 • Shaoxiong Ji, Wei Sun, Xiaobo Li, Hang Dong, Ara Taalas, Yijia Zhang, Honghan Wu, Esa Pitkänen, Pekka Marttinen
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
no code implementations • LREC 2022 • Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, Erik Cambria
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.
no code implementations • 7 Sep 2021 • Shaoxiong Ji, Pekka Marttinen
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.
2 code implementations • 6 Sep 2021 • Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
no code implementations • 26 Aug 2021 • Bruce Nguyen, Shaoxiong Ji
The massive scale and growth of textual biomedical data have made its indexing and classification increasingly important.
1 code implementation • 2 Apr 2021 • Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
no code implementations • 11 Mar 2021 • Shaoxiong Ji, Matti Hölttä, Pekka Marttinen
In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries.
no code implementations • 25 Feb 2021 • Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang, Yixin Liu, Lauri Vasankari, Shirui Pan, Guodong Long, Anwar Walid
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
no code implementations • Findings (ACL) 2021 • Shaoxiong Ji, Shirui Pan, Pekka Marttinen
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.
no code implementations • EMNLP (ClinicalNLP) 2020 • Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.
1 code implementation • 31 May 2020 • Wei Li, Wei Shao, Shaoxiong Ji, Erik Cambria
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.
Ranked #39 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • 16 Apr 2020 • Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.
no code implementations • 21 Mar 2020 • Shaoxiong Ji, Wenqi Jiang, Anwar Walid, Xue Li
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.
1 code implementation • 2 Feb 2020 • Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
no code implementations • 23 Oct 2019 • Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, Zi Huang
Suicide is a critical issue in modern society.
4 code implementations • 17 Dec 2018 • Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.