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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2022 (2022)
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| 2 |
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
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| 3 |
Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation
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| 4 |
Multiplicative Linear Logic from Logic Programs and Tilings
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In: https://hal.archives-ouvertes.fr/hal-02895111 ; 2021 (2021)
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| 5 |
A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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| 6 |
Stellar Resolution: Multiplicatives - for the linear logician, through examples
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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| 7 |
A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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| 8 |
Stellar Resolution: Multiplicatives - for the linear logician, through examples
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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| 10 |
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ...
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| 11 |
History of Logo
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In: Proceedings of the ACM on Programming Languages, vol 4, iss HOPL (2020)
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| 12 |
Preventing Summer Reading Slide: Examining the Effects of Two Computer-Assisted Reading Programs
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In: ETSU Faculty Works (2020)
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| 13 |
A call for cautious interpretation of meta-analytic reviews
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In: Education Publications (2020)
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| 14 |
Mechanized metatheory revisited
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In: ISSN: 0168-7433 ; EISSN: 1573-0670 ; Journal of Automated Reasoning ; https://hal.inria.fr/hal-01884210 ; Journal of Automated Reasoning, Springer Verlag, 2019, 63 (3), pp.625-665. ⟨10.1007/s10817-018-9483-3⟩ (2019)
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| 15 |
Decidable XPath Fragments in the Real World
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In: 38th ACM Symposium on Principles of Database Systems (PODS'19) ; https://hal.inria.fr/hal-01852475 ; 38th ACM Symposium on Principles of Database Systems (PODS'19), 2019, Amsterdam, Netherlands. ⟨10.1145/3294052.3319685⟩ (2019)
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| 16 |
Primärprozess in der Katathym Imaginativen Psychotherapie unter dem Einfluss psychotroper Substanzen ...
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| 17 |
The Manifesto Corpus: a new resource for research on political parties and quantitative text analysis
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In: Research and Politics ; 3 ; 2 ; 1-8 (2019)
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| 18 |
A sequent calculus with dependent types for classical arithmetic
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In: LICS 2018 - 33th Annual ACM/IEEE Symposium on Logic in Computer Science ; https://hal.inria.fr/hal-01703526 ; LICS 2018 - 33th Annual ACM/IEEE Symposium on Logic in Computer Science, Jul 2018, Oxford, United Kingdom. pp.720-729, ⟨10.1145/3209108.3209199⟩ (2018)
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| 19 |
NgramPOS: A Bigram-based Linguistic and Statistical Feature Process Model for Unstructured Text Classification
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Abstract:
Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. With the ever growing social inetworking and online marketing sites, the reviews obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like clustering or classification to provide different polarity categories such as positive and negative in order to extract a meaningful information for future uses. Accordingly, in this study we investigate the use of Natural Language processing (NLP) in a way to improve the sentiment classification performance to evaluate the information content of financial news as an instrument for using in investmentdecisions system.Since the proposed feature extraction approach is based on the occurrence frequency of words, low-frequency linguist features that could be critical in sentiment classification are typically ignored. In this research, therefore, we attempt to improve current sentiment analysis approaches for financial news classification in consideration of low-frequency, informative, linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for text sentiment analysis and classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy than other types feature sets.
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Keyword:
005 Computer programming; Bigram-based Linguistic and Statistical Feature; Centre for Distributed Computing; Information science; Information Society; Networking and Security; programs & data; QA75 Electronic computers. Computer science; Unstructured Text Classification
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URL: https://napier-surface.worktribe.com/1250784/1/NgramPOS%3A%20A%20Bigram-based%20Linguistic%20and%20Statistical%20Feature.
http://researchrepository.napier.ac.uk/Output/1250784
https://doi.org/10.1007/s11276-018-01909-0
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| 20 |
ПРОФЕССИОНАЛЬНО-ОРИЕНТИРОВАННОЕ ОБУЧЕНИЕ ИНОСТРАННОМУ ЯЗЫКУ МАГИСТРАНТОВ ТЕХНИЧЕСКИХ СПЕЦИАЛЬНОСТЕЙ
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