Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (I) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert's a priori belief about a word's meaning can be incorrect -- annotation should be performed based on the word's contexts in the target domain instead.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationSimilar Papers 제목 키워드 기반
Boosting Domain Adaptation by Discovering Latent Domains
Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice, most datasets can be regarded as mixtures of multiple domains. In …
Domain AdaptationDomain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentation, performance often degrades when algor…
ClassificationDomain AdaptationGeneral ClassificationOrgan Segmentation+2Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new dens…
Automatic Cell CountingDomain AdaptationregressionEduMT: Developing Machine Translation System for Educational Content in Indian Languages
In this paper, we explore various approaches to build Hindi to Bengali Neural Machine Translation (NMT) systems for the educational domain. Translation of educational content poses several challenges, such as unavailabil…
Data AugmentationDomain AdaptationMachine TranslationNMT+1Adversarial Domain Adaptation for Duplicate Question Detection
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is ver…
Domain AdaptationQuestion Similarity