In this paper, we propose an unsupervised method to identify noun sense
changes based on rigorous analysis of time-varying text data available in the
form of millions of digitized books. We construct distributional thesauri based
networks from data at different time points and cluster each of them separately
to obtain word-centric sense clusters corresponding to the different time
points. Subsequently, we compare these sense clusters of two different time
points to find if (i) there is birth of a new sense or (ii) if an older sense
has got split into more than one sense or (iii) if a newer sense has been
formed from the joining of older senses or (iv) if a particular sense has died.
We conduct a thorough evaluation of the proposed methodology both manually as
well as through comparison with WordNet. Manual evaluation indicates that the
algorithm could correctly identify 60.4% birth cases from a set of 48 randomly
picked samples and 57% split/join cases from a set of 21 randomly picked
samples. Remarkably, in 44% cases the birth of a novel sense is attested by
WordNet, while in 46% cases and 43% cases split and join are respectively
confirmed by WordNet. Our approach can be applied for lexicography, as well as
for applications like word sense disambiguation or semantic search.

本文提出了一种无监督的方法来区分名词的意义变化，并通过数字化书籍中的严格时间变化文本数据构建分布式词库网络，将它们分别聚类以获得与不同时间点对应的以词为中心的意义聚类，这种方法可应用于词汇编纂和语义搜索，并经过人工评估和 WordNet 对比，该算法在 48 个样本和 21 个样本分别中正确识别出 60.4％的新出现情况，57％的分裂 / 合并情况，并有 44％的新意义得到 WordNet 验证。