Ma Junteng


pdf bib
Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-based Sentiment Analysis
Wang Junlang | Li Xia | He Junyi | Zheng Yongqiang | Ma Junteng
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“Implicit sentiment modeling in aspect-based sentiment analysis is a challenging problem due tocomplex expressions and the lack of opinion words in sentences. Recent efforts focusing onimplicit sentiment in ABSA mostly leverage the dependency between aspects and pretrain onextra annotated corpora. We argue that linguistic knowledge can be incorporated into the modelto better learn implicit sentiment knowledge. In this paper, we propose a PLM-based, linguis-tically enhanced framework by incorporating Part-of-Speech (POS) for aspect-based sentimentanalysis. Specifically, we design an input template for PLMs that focuses on both aspect-relatedcontextualized features and POS-based linguistic features. By aligning with the representationsof the tokens and their POS sequences, the introduced knowledge is expected to guide the modelin learning implicit sentiment by capturing sentiment-related information. Moreover, we alsodesign an aspect-specific self-supervised contrastive learning strategy to optimize aspect-basedcontextualized representation construction and assist PLMs in concentrating on target aspects. Experimental results on public benchmarks show that our model can achieve competitive andstate-of-the-art performance without introducing extra annotated corpora.”


pdf bib
Fundamental Analysis based Neural Network for Stock Movement Prediction
Zheng Yangjia | Li Xia | Ma Junteng | Chen Yuan
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“Stock movements are influenced not only by historical prices, but also by information outside the market such as social media and news about the stock or related stock. In practice, news or prices of a stock in one day are normally impacted by different days with different weights, and they can influence each other. In terms of this issue, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. First, we propose three new technical indicators based on raw prices according to the finance theory as the basic encode of the prices of each day. Then, we introduce a coattention mechanism to capture the sufficient context information between text and prices across every day within a time window. Based on the mutual promotion and influence of text and price at different times, we obtain more sufficient stock representation. We perform extensive experiments on the real-world StockNet dataset and the experimental results demonstrate the effectiveness of our method.”