Information Navigation using Statistical Rhymes

该项目基于以下假设：统计上有意义的共现频率的单词可以作为有用信息导航系统的指南，即使这些共现不是基于语义相似性或关联性。我们称这样的共现为统计押韵。我们一直试图用贝叶斯概率模型来提取统计押韵。因此，我们成功地提出了一种新的类LDA(潜在狄利克雷分配)的主题提取方法，该方法可以对书目数据中出现的单词标记序列进行切分，我们可以在学术论文的参考文献部分或研究人员网站的出版物部分观察到这种切分。该方法将每个书目数据分成不同的数据段，每个段对应于不同的数据字段，如作者、论文标题、期刊、页面、出版年份等。此外，我们还通过半监督推理提高了切分精度。

This project is based on the following assumption : Words that co-occur in statistically significant frequency can be used as a guide in useful information navigation system even when those co-occurrences are not based on semantic similarity or relatedness. We call such co-occurrences statistical rhyme. We have been trying to extract statistical rhymes with Bayesian probabilistic models. We consequently succeeded in proposing a new LDA(latent Dirichlet allocation)-like topic extraction method that can give a segmentation of word token sequences appearing in bibliographic data, which we can observe in references section of academic papers or in publications section of researchers' Web sites. Our method split each bibliographic data into the segments each corresponding to different data field, e. g. authors, paper title, journal, pages, publication year, etc. Further, we improved segmentation accuracy by making the inference semi-supervised.