2 research outputs found
Enhancement of Short Text Clustering by Iterative Classification
Short text clustering is a challenging task due to the lack of signal
contained in such short texts. In this work, we propose iterative
classification as a method to b o ost the clustering quality (e.g., accuracy)
of short texts. Given a clustering of short texts obtained using an arbitrary
clustering algorithm, iterative classification applies outlier removal to
obtain outlier-free clusters. Then it trains a classification algorithm using
the non-outliers based on their cluster distributions. Using the trained
classification model, iterative classification reclassifies the outliers to
obtain a new set of clusters. By repeating this several times, we obtain a much
improved clustering of texts. Our experimental results show that the proposed
clustering enhancement method not only improves the clustering quality of
different clustering methods (e.g., k-means, k-means--, and hierarchical
clustering) but also outperforms the state-of-the-art short text clustering
methods on several short text datasets by a statistically significant margin.Comment: 30 pages, 2 figure
Approaches for the clustering of geographic metadata and the automatic detection of quasi-spatial dataset series
The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning