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#textannotation

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Staatsbibliothek zu Berlin

Meistert mit uns die Herausforderungen der automatischen #Textannotation historischer Sprachen: In unserem Online- #Workshop "Automatische Annotation von Kirchenslavica mit Stanza" (Mo., 10. Februar) lernt ihr, wie ihr mit der #Python-Bibliothek #Stanza eure Kenntnisse im Bereich Natural Language Processing (#NLP) mit einem Fokus auf die Annotation von #Kirchenslavica erweitern könnt 👉 blog.sbb.berlin/online-worksho

Lukas Isermann

Thread:
🚨Software Publication Alert🚨
This one is for you, text-analysis folks:

I am very happy to share the publication of my text annotation R-package "handcodeR" 🥳, which is now available on CRAN. 🧵1/7

CRAN.R-project.org/package=handcoge=handcodeR

#CRAN#rstats#TextAsData
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Erik Jonker

Interesting but not surprising at all, this is a typical task you would want to use these models for: "ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks" , it could give an enormous boost to annotation of data, which can be used for AI training. Further reinforcing the speed of AI development. Footnote, this was done with GPT3.5 not with GPT4 that is available now and better.
#AI #ChatGPT #GPT #crowdworkers #textannotation
arxiv.org/abs/2303.15056

arXiv.orgChatGPT Outperforms Crowd-Workers for Text-Annotation TasksMany NLP applications require manual data annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using a sample of 2,382 tweets, we demonstrate that ChatGPT outperforms crowd-workers for several annotation tasks, including relevance, stance, topics, and frames detection. Specifically, the zero-shot accuracy of ChatGPT exceeds that of crowd-workers for four out of five tasks, while ChatGPT's intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk. These results show the potential of large language models to drastically increase the efficiency of text classification.
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