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--- nlp:rule-based_systems [2021/10/26 06:34] – [Papers] jmflanig
+++ nlp:rule-based_systems [2023/06/15 07:36] (current) – external edit 127.0.0.1
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 ===== Papers =====
-  * Information Extraction
+  * [[Information Extraction]]
+    * See Chapter 2 of [[https://www.cis.uni-muenchen.de/~fraser/information_extraction_2020_lecture/sarawagi.pdf|Sarawagi 2008 - Information Extraction]]
     * [[https://aclanthology.org/D13-1079.pdf|Chiticariu et al 2013 - Rule-based Information Extraction is Dead! Long Live Rule-based Information Extraction Systems!]]
     * [[https://www.sciencedirect.com/science/article/pii/S1532046419300218|Topaz et al 2019 - Mining fall-related information in clinical notes: Comparison of rule-basedand novel word embedding-based machine learning approaches]]
-  * Machine Translation
+  * [[Machine Translation]]
     * [[https://aclanthology.org/www.mt-archive.info/MTS-1995-Boguslavsky.pdf|Boguslavsky 1995 - A Bi-directional Russian-to-English Machine Translation System (ETAP-3)]] and [[http://proling.iitp.ru/etap4|ETAP-4]] These are highly developed rule-based broad coverage Russian-English MT systems
-  * Sentiment Analysis
+  * [[Sentiment Analysis]]
     * [[http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf|Hutto & Gilbert 2013 - VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text]] This paper shocked everyone because it was SOTA at the time. It was a rule-based!
     * [[https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.02.002/full/pdf?title=a-mixed-approach-of-deep-learning-method-and-rule-based-method-to-improve-aspect-level-sentiment-analysis|Ray & Chakrabarti et al 2019 - A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis]]
-  * Morphological Analysis
+  * [[Morphological Analysis]]
     * The most common approach to morphological analysis is a rule-based [[fsas_and_fsts|FST]] system
   * [[Grammatical Error Correction]]
     * Some systems are rule-based, see [[https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E1B54FD65963E65D46EF440B5A13F186/S1351324921000164a.pdf/div-class-title-the-automated-writing-assistance-landscape-in-2021-div.pdf|Dale & Viethen 2021 - The Automated Writing Assistance Landscape in 2021]]