Editing Natural language processing (section)

=== Neural networks ===
{{Further|Artificial neural network}}
A major drawback of statistical methods is that they require elaborate [[feature engineering]]. Since 2015,<ref>{{Cite web |last=Socher |first=Richard |title=Deep Learning For NLP-ACL 2012 Tutorial |url=https://www.socher.org/index.php/Main/DeepLearningForNLP-ACL2012Tutorial |access-date=2020-08-17 |website=www.socher.org}} This was an early Deep Learning tutorial at the ACL 2012 and met with both interest and (at the time) skepticism by most participants. Until then, neural learning was basically rejected because of its lack of statistical interpretability. Until 2015, deep learning had evolved into the major framework of NLP. [Link is broken, try http://web.stanford.edu/class/cs224n/]</ref> the statistical approach has been replaced by the [[Artificial neural network|neural networks]] approach, using [[semantic networks]]<ref>{{cite book |last1=Segev |first1=Elad |title=Semantic Network Analysis in Social Sciences |date=2022 |publisher=Routledge |location=London |isbn=9780367636524 |url=https://www.routledge.com/Semantic-Network-Analysis-in-Social-Sciences/Segev/p/book/9780367636524 |access-date=5 December 2021 |archive-date=5 December 2021 |archive-url=https://web.archive.org/web/20211205140726/https://www.routledge.com/Semantic-Network-Analysis-in-Social-Sciences/Segev/p/book/9780367636524 |url-status=live }}</ref> and [[word embedding]]s to capture semantic properties of words.  

Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) are not needed anymore. 

[[Neural machine translation]], based on then-newly invented [[Seq2seq|sequence-to-sequence]] transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for [[statistical machine translation]].