A Natural Language Process-Based Framework for Automatic Association Word Extraction

Word association, revealing mental representations and connections of human, has been widely studied in psychology. However, the scale of available associative cue-response words is severely restricted due to the traditional manually collecting methodology. Meanwhile, with the tremendous success in Natural Language Process (NLP) tasks, an extremely large amount of plain texts can be easily acquired. This suggests an insight about the potential to find association words automatically from the text corpus instead of manually collection. As an original attempt, this paper takes a small step toward proposing a deep learning based framework for automatic association word extraction. The framework mainly consists of two stages of association word detection and machine association network construction. In particular, attention mechanism based Reading Comprehension (RC) algorithm is explored to find valuable association words automatically. To validate the value of the extracted association words, the correlation coefficient between semantic similarities of machine and human association words is introduced as an effective measurement for evaluating association consistence. The experiments are conducted on two text datasets from which together about 20k association words, more than the existing largest human association word dataset, are finally derived. The experiment further verifies that the machine association words are generally consistent with human association words with respect to semantic similarity, which highlights the promising utilization of the machine association words in the future researches of both psychology and NLP.