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.

本描述呈现了通过词汇搜索对梦境内容进行分析的SALAD算法的实现。辅助构建初始种子词字典的函数收录于"hyponym_dictionary.py"中，该文件亦将用于从种子词构建字典。"read_csv.py"负责读取并预处理梦境报告，将其转化为字典，该字典能够捕捉梦境中词汇与句子的语言特征。此外，该文件还包含改进后的Lesk算法的实现。"Series/"文件夹可填充来自任何梦境日记的数据（您可以从www.dreambank.net获取数据）。所需数据格式为每行包含一个梦境的csv文件。"search_lemmas.py"代码执行实际的词汇搜索。SALAD算法的具体步骤以及为获得最佳结果所需调整的参数均在论文中进行了详细描述。代码采用Python 3.6编写，可在Python 3.6及以上版本运行。