Data for: Semi-Supervised Lexicon Generation Using Semantic Relations for Dream Content Analysis

Published: 17 Jan 2020 | Version 1 | DOI: 10.17632/ftbdx4zcx7.1
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Description of this data

Presented is an implementation of the SALAD algorithm for dream content analysis through word searching. Helper functions for constructing initial seed word dictionaries are provided in "hyponym_dictionary.py" which will also be used to construct the dictionaries from the seed words. "read_csv.py" reads and pre-processes the dream reports into a dictionary that captures the linguistic features of the words and sentences from the dreams. It also contains an implementation of the Improved Lesk Algorithm. The folder Series/ can be populated with data from any dream journal (you can take data from www.dreambank.net). The required data format is a csv file containing one dream in each row. The code "search_lemmas.py" performs the actual word search. The exact steps of SALAD and the parameters that need to be played around with to obtain the best results are described in the paper. The codes are written in Python 3.6 and can run on Python 3.6 and above.

Experiment data files

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Latest version

  • Version 1

    2020-01-17

    Published: 2020-01-17

    DOI: 10.17632/ftbdx4zcx7.1

    Cite this dataset

    Sood, Pourush; Brahma, Kaustav (2020), “Data for: Semi-Supervised Lexicon Generation Using Semantic Relations for Dream Content Analysis”, Mendeley Data, v1 http://dx.doi.org/10.17632/ftbdx4zcx7.1

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Institutions

Indian Institute of Technology Kharagpur

Categories

Natural Language Processing, Dream Studies, Dreaming, Word List

Licence

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The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

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