Titlebook: Machine Learning for Text; Charu C. Aggarwal Textbook 2022Latest edition Springer Nature Switzerland AG 2022 Machine Learning.Deep Learnin

TEN

Text Clustering,The problem of text clustering is that of partitioning a corpus into groups of similar documents. Clustering is an . application because no data-driven guidance is provided about specific types of groups (e.g., sports, politics, and so on) with the use of training data.

Painstaking

Linear Models for Classification and Regression,Linear models for classification and regression express the dependent variable (or class variable) as a linear function of the independent variables (or feature variables). Specifically, consider the case in which .. is the dependent variable of the .th document, and . are the .-dimensional feature variables of this document.

慢跑鞋

Classifier Performance and Evaluation,Among all machine learning problems, classification is the most well studied, and has the most number of solution methodologies. This embarrassment of riches also leads to the natural problems of model selection and ..

intangibility

Joint Text Mining with Heterogeneous Data,Text documents often occur in combination with other heterogeneous data such as images, Web links, social media, ratings, and so on.

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enhance

Text Summarization,Text summarization creates a short summary of a document, which can be easily assimilated by the user. The most basic form of text summarization creates a summary from a single document, although it is also possible to do so from multiple documents. The key applications of text summarization are as follows:

commute

DOLT

Text Segmentation and Event Detection,Although text segmentation and event detection might seem like different problems, they are closely related. In both cases, the text in one or more documents is scanned sequentially in order to detect key changes. Therefore, the concept of . is the overarching theme of this chapter.

阻止

飛鏢

Attention Mechanisms and Transformers, of the data, so that portions of the data that are most relevant for prediction are emphasized. A classical example of an application of attention occurs in machine translation, where the translation of a specific part of a target sentence often focuses on important parts of the source sentence.