Previous chapters overviewed a number of NLP tasks, from binary classification tasks, such as author identification and sentiment analysis, to multiclass classification tasks, such as topic analysis. These applications deployed machine-learning models and relied on a range of linguistic features, most often related to words or word characteristics. While it is true that individual words express information useful in the context of many NLP applications, often the information-bearing unit is actually larger than a single word. In chapter 4, you looked into the task of information extraction. Remember that this task allows you to extract facts and relevant information from an otherwise unstructured data, such as raw, unprocessed text. This task is instrumental in a number of applications, from information management to database completion to question answering. For instance, suppose you have a collection of texts on various personalities, including the Wikipedia article on Albert Einstein (https://en.wikipedia.org/wiki/Albert_Einstein). Figure 11.1 shows a sentence from this article.
