A Pseudo Label based Dataless Naive Bayes Algorithm for Text Classification with Seed Words
Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationPseudo Labeltext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Practical Dataless Text Classification Through Dense Retrieval
Dataless text classification aims to classify documents using only class descriptions without any training data. Recent research shows that pre-trained textual entailment models can achieve state-of-the-art dataless cla…
ClassificationNatural Language InferenceRetrievaltext-classification+1Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals
Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words of categories, known as seed words. The …
text-classificationText ClassificationWeakly-supervised LearningUnsupervised Label Refinement Improves Dataless Text Classification
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descri…
ClassificationClusteringGeneral ClassificationText Classification+1A Semi-Supervised Adaptive Discriminative Discretization Method Improving Discrimination Power of Regularized Naive Bayes
Recently, many improved naive Bayes methods have been developed with enhanced discrimination capabilities. Among them, regularized naive Bayes (RNB) produces excellent performance by balancing the discrimination power an…
Improved Naive Bayes with Mislabeled Data
Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose …
Classificationtext-classificationText Classification