Although supervised techniques, such as classification, have reigned supreme over the last few years in the industry, the potential of unsupervised techniques such as text clustering cannot be understated.
Text clustering aims to group similar texts based on their semantic content, meaning, and relationships. As illustrated in Figure 5-1, the resulting clusters of semantically similar documents not only facilitate efficient categorization of large volumes of unstructured text but also allow for quick exploratory data analysis.
Figure 5-1. Clustering unstructured textual data.
The recent evolution of language models, which enable contextual and semantic representations of text, has enhanced the effectiveness of text clustering. Language is more than a bag of words, and recent language models have proved to be quite capable of capturing that notion. Text clustering, unbound by supervision, allows for creative solutions and diverse applications, such as finding outliers, speedup labeling, and finding incorrectly labeled data.
Text clustering has also found itself in the realm of topic modeling, where we want to discover (abstract) topics that appear in large collections of textual data. As shown in Figure 5-2, we generally describe a topic using keywords or keyphrases and, ideally, have a single overarching label.
Figure 5-2. Topic modeling ...
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