| Challenge: | a recent announcement of a state plan to build a new economic region has led to the rise of hundreds of stocks . concepts can be useful for investors to find out relevant concept stocks for making investment decisions . a chinese research team uses deep learning to mine evidences from large textual data . |
| Approach: | They use distributed word similarities and deep reinforcement learning to learn a strategy of topic expansion from large scale textual data. |
| Outcome: | The proposed method outperforms a baseline method on two Chinese stock market datasets. |
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Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
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Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain (2024.lrec-main)
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| Challenge: | Argument mining is a complex process that requires a large amount of resources and time. |
| Approach: | They propose to analyze arguments in three different languages and domains to understand their robustness to natural language variations. |
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Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)
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| Challenge: | Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors. |
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Financial Opinion Mining (2021.emnlp-tutorials)
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| Challenge: | This tutorial will provide an overview of financial opinion mining and provide research directions. |
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Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
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Transferring Confluent Knowledge to Argument Mining (2022.coling-1)
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| Challenge: | Argument mining is a natural language processing task that seeks to obtain structured arguments from unstructured text. |
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| Outcome: | The proposed method dispenses with heavy feature and model engineering and allows for new state-of-the-art performance for its three main sub-tasks. |
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction (2023.findings-acl)
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| Challenge: | Existing methods for semantic frame induction are labor intensive . a method that uses contextualized embeddings can be used to acquire frame element knowledge. |
| Approach: | They propose a method that applies deep metric learning to semantic frame induction tasks . they use a pre-trained language model to fine-tune frame-annotated models to perform argument clustering . |
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Quantitative Day Trading from Natural Language using Reinforcement Learning (2021.naacl-main)
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| Challenge: | Existing approaches to stock prediction are not optimized to make profitable investment decisions. |
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Multi-Task Learning for Argumentation Mining in Low-Resource Settings (N18-2)
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| Challenge: | Argument component identification is difficult for trained annotators to perform in a new domain or to develop new AM tasks. |
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Semantic search with domain-specific word-embedding and production monitoring in Fintech (2020.coling-demos)
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| Challenge: | a novel system with domain-specific custom language models for accurate search terms expansion addresses several challenges faced in an industry-setting . many industry-grade search engines exist, but their accuracy can be sub-optimal due to domain specificity and terms provided by the users. |
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