نتایج جستجو برای: text classification rocchio
تعداد نتایج: 641860 فیلتر نتایج به سال:
Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and its tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be ...
Bag-of-words representations are used in many NLP applications, such as text classification and sentiment analysis. These representations ignore relations across different sentences in a text and disregard the underlying structure of documents. In this work, we present a method for text classification that takes into account document structure and only considers segments that contain informatio...
This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models, the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each s...
In this paper I discuss the application of stochastic blockmodeling to the domain of legal opinions, and specifically to the end of classifying those legal opinions into topics. I begin by defining the basic social network problem and the stochastic blockmodel in Section 2. In Section 3 I introduce the problem of labeling legal opinions and discuss the particular features of this domain. Sectio...
Text mining and Text classification are the two prominent and challenging tasks in the field of Machine learning. Text mining refers to the process of deriving high quality and relevant information from text, while Text classification deals with the categorization of text documents into different classes. The real challenge in these areas is to address the problems like handling large text corp...
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