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Local-Global Context Aware Transformer for Language-Guided Video Segmentation ...
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Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning ...
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Cumulative Effects of Physical, Chemical, and Biological Measures on Algae Growth Inhibition
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In: Water; Volume 14; Issue 6; Pages: 877 (2022)
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Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation ...
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CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes ...
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Constructing a Psychometric Testbed for Fair Natural Language Processing ...
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Juegos serios en web para la auto-protección y prevención del COVID-19: Desarrollo y pruebas de usabilidad
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 69, 2021 (Ejemplar dedicado a: Participación ciudadana en la esfera digital), pags. 97-111 (2021)
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ActBERT: Learning Global-Local Video-Text Representations ...
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Speech-to-Singing Conversion in an Encoder-Decoder Framework ...
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Symbiotic Attention with Privileged Information for Egocentric Action Recognition ...
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Grounded and Controllable Image Completion by Incorporating Lexical Semantics ...
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The Informativeness of Text, the Deep Learning Approach
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Abstract:
This paper uses a deep learning natural language processing approach (Google's Bidirectional Encoder Representations from Transformers, hereafter BERT) to comprehensively summarize financial texts and examine their informativeness. First, we compare BERT's effectiveness in sentiment classification in financial texts with that of a finance specific dictionary, the naïve Bayes, and Word2Vec, a shallow machine learning approach. We find that first, BERT outperforms all other approaches, and second, pre-training BERT with financial texts further improves its performance. Using BERT, we show that conference call texts provide information to investors and that other less accurate approaches underestimate the economic significance of textual informativeness by at least 25%. Last, textual sentiments summarized by BERT can predict future earnings and capital expenditure, after controlling for financial statement based determinants commonly used in finance and accounting research.
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Keyword:
Capital Investment; Deep Learning; Earnings; Informativeness; Machine Learning; Natural Language Processing; Textual Analysis
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URL: http://hdl.handle.net/10125/70549
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Measurement of $W^{\pm}$-boson and $Z$-boson production cross-sections in $pp$ collisions at $\sqrt{s}=2.76$ TeV with the ATLAS detector
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Baidu-UTS Submission to the EPIC-Kitchens Action Recognition Challenge 2019 ...
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Китайско-русский параллельный дискурсивный корпус: выравнивание на уровне клаузы и статистический анализ ; Chinese-Russian Parallel Discourse Corpus: Alignment of Clauses and Statistical Analysis
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Extensive translation of circular RNAs driven by N6-methyladenosine
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Китайско-русский параллельный корпус с дискурсивно-структурной разметкой: теоретические аспекты ; Theoretical aspects of building a Chinese-Russian Parallel Corpus with discourse-structure annotation
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