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

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.

本套件实现了用于通过词汇检索开展梦境内容分析的SALAD算法（SALAD algorithm）。文件"hyponym_dictionary.py"提供了构建初始种子词汇词典的辅助函数，且该文件同样可基于种子词汇完成词典的构建工作。"read_csv.py"用于读取并预处理梦境报告，将其转换为可捕获梦境中词汇与语句语言学特征的词典；该模块同时实现了改进型莱斯科算法（Improved Lesk Algorithm）。Series/文件夹可填入任意梦境日记数据集，数据可从www.dreambank.net获取，所需数据格式为csv文件，每行存储一则梦境。"search_lemmas.py"负责执行实际的词汇检索操作。SALAD算法的具体实现步骤与为获取最优结果所需调整的参数，已在相关论文中详细说明。本套代码基于Python 3.6编写，可在Python 3.6及以上版本环境中运行。