Search Results for author:
Found 14 papers, 4 papers with code
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis
Both the issues of data deficiencies and semantic consistency are important for data augmentation.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder
In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community.
Financial Opinion Mining
In this tutorial, we will show where we are and where we will be to those researchers interested in this topic.
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
In this paper, we address the hallucination problem commonly found in natural language generation tasks.
NumHG: A Dataset for Number-Focused Headline Generation
Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text.
Entity-Aware Dual Co-Attention Network for Fake News Detection
Fake news and misinformation spread rapidly on the Internet.
FinTech for Social Good: A Research Agenda from NLP Perspective
Making our research results positively impact on society and environment is one of the goals our community has been pursuing recently.
Dynamic Graph Transformer for Implicit Tag Recognition
Textual information extraction is a typical research topic in the NLP community.
Fine-grained Financial Opinion Mining: A Survey and Research Agenda
Opinion mining is a prevalent research issue in many domains.
NLP in FinTech Applications: Past, Present and Future
Financial Technology (FinTech) is one of the worldwide rapidly-rising topics in the past five years according to the statistics of FinTech from Google Trends.
Issues and Perspectives from 10,000 Annotated Financial Social Media Data
In this paper, we investigate the annotation of financial social media data from several angles.
Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments
In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description.
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Numerals that contain much information in financial documents are crucial for financial decision making.
NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Short length, multi-targets, target relation-ship, monetary expressions, and outside reference are characteristics of financial tweets.
