Multi-value Classification of Very Short Texts

Abstract

Abstract. We introduce a new stacking-like approach for multi-value classification. We apply this classification scheme using Naive Bayes, Rocchio and kNN classifiers on the well-known Reuters dataset. We use part-of-speech tagging for stopword removal. We show that our setup performs almost as well as other approaches that use the full article text even though we only classify headlines. Finally, we apply a Rocchio classifier on a dataset from a Web 2.0 site and show that it is suitable for semi-automated labelling (often called tagging) of short texts and is faster than other approaches.

Using Titles vs. Full-text as Source for Automated Semantic Document Annotation

“…The most prominent approach to adapt classifiers for multi-labeling is binary relevance [26,28]. Other options include the chaining [21] as well as stacking [9,27] of classifiers. While the former is not well-suited for high amounts of considered labels, we also include a variation of the latter idea in our comparison.…”

“…As meta-classifiers, we use decision trees with Gini impurity as splitting criterion. To limit complexity, we generate training data only for those meta-classifiers, whose class is among the top 30 of the base-classifier's ranking [9]. We use this decision tree module (abbreviated with the suffix *DT) as an alternative to hard cut-offs in Learning to Rank (see Section 2, and the fixed thresholds in multi-layer perceptrons (see Section 3.2.2).…”

“…We use this decision tree module (abbreviated with the suffix *DT) as an alternative to hard cut-offs in Learning to Rank (see Section 2, and the fixed thresholds in multi-layer perceptrons (see Section 3.2.2). For comparison with the original work of Heß et al [9], we also consider Rocchio as a base-classifier. We furthermore experiment with applying the decision tree module on top of binary-relevance logistic regression.…”

“…We used the annotations provided by the indexing service because it is reasonable to expect that they are more consistent and of higher quality (cf. [9]). As for the Reuters dataset, we chose a random subset of 100, 000 documents containing both full-text and titles.…”

N-Gram Representations For Comment Filtering

“…comments, reviews or web searches) has been intensively studied since 2008 [15,46,27]. There are great benefits in being able to analyse short texts, for example, advertisers might be interested in the sentiment of product reviews on e-commerce sites to more efficiently pair marketing material to content.…”