Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lee, Meisin"'
Autor:
Lee, Meisin, Lay-Ki, Soon
This paper presents our participation under the team name `Finance Wizard' in the FinNLP-AgentScen 2024 shared task #2: Financial Text Summarization. It documents our pipeline approach of fine-tuning a foundation model into a task-specific model for
Externí odkaz:
http://arxiv.org/abs/2408.03762
One of the challenges in event extraction via traditional supervised learning paradigm is the need for a sizeable annotated dataset to achieve satisfactory model performance. It is even more challenging when it comes to event extraction in the financ
Externí odkaz:
http://arxiv.org/abs/2205.00387
In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serve to contribute towards resource building for economic and financial text mining. This paper descr
Externí odkaz:
http://arxiv.org/abs/2204.03871
Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning ev
Externí odkaz:
http://arxiv.org/abs/2109.12781
Commodity News contains a wealth of information such as sum-mary of the recent commodity price movement and notable events that led tothe movement. Through event extraction, useful information extracted fromcommodity news is extremely useful in minin
Externí odkaz:
http://arxiv.org/abs/2105.08214
Autor:
Lee, Meisin
The thesis is about using news events to predict crude oil prices. The three research objectives of this research are: (1) build an annotated crude oil dataset for event extraction, (2) train machine learning models on event extraction and (3) use ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5896fc5518cba588585a3acc0f746dc4