Large scale biomedical texts classification: a kNN and an ESA-based approaches

Abstract

BackgroundWith the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this direction, it still remains a real challenge. Moreover, the issue is even more complex as full texts are not always freely available. Then, using only partial information to annotate these documents is promising but remains a very ambitious issue.MethodsWe propose two classification methods: a k-nearest neighbours (kNN)-based approach and an explicit semantic analysis (ESA)-based approach. Although the kNN-based approach is widely used in text classification, it needs to be improved to perform well in this specific classification problem which deals with partial information. Compared to existing kNN-based methods, our method uses classical Machine Learning (ML) algorithms for ranking the labels. Additional features are also investigated in order to improve the classifiers’ performance. In addition, the combination of several learning algorithms with various techniques for fixing the number of relevant topics is performed. On the other hand, ESA seems promising for this classification task as it yielded interesting results in related issues, such as semantic relatedness computation between texts and text classification. Unlike existing works, which use ESA for enriching the bag-of-words approach with additional knowledge-based features, our ESA-based method builds a standalone classifier. Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach.ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0.55 % while the ESA-based approach surprisingly yielded unsatisfactory results.ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information. They are therefore adequate for large multi-label classification and particularly in the biomedical domain. Thus, our work contributes to the extraction of relevant information from unstructured documents in order to facilitate their automated processing. Consequently, it could be used for various purposes, including document indexing, information retrieval, etc.

On classification of abstracts obtained from medical journals

“…They reported that the proposed methodology indicated reasonable classification performance. Drame et al [28] proposed k -nearest neighbours (kNN) and an explicit semantic analysis based approach for large-scale biomedical document classification on a subset of MEDLINE documents. They stated that the kNN-based method with the RF learning algorithm achieved good performances compared with the current state-of-the-art methods.…”

Taxonomic Corpus-Based Concept Summary Generation for Document Annotation

“…Recall value is more relevant in this case since it shows the proportion of the correct annotations that an approach was able to discover. The choice of concepts for annotating documents is quite subjective and attaining high recall values remain a challenge [8].…”

“…One variant of KNN ranks candidate concepts by combining the relevance scores of documents for which they form annotations [6,7]. Another variant passes the features of candidate concepts to a machine classifier which determines which concepts to put forward for annotation [8]. Some features that are used by a classifier include the proportion of retrieved documents that were annotated with the concept and if the concept appears in the title or content of a document.…”

“…Some features that are used by a classifier include the proportion of retrieved documents that were annotated with the concept and if the concept appears in the title or content of a document. Experimental results show that KNN or hybrids of it are most effective in recommending annotations [2,8]. However, supervised approaches cannot be used when a corpus of annotated documents does not exist.…”

Towards optimize-ESA for text semantic similarity: A case study of biomedical text

“…Furthermore, WikiRelate [7] was the first work which compute the measures of semantic relatedness using Wikipedia, this approach applied the familiar technique used in semantic relatedness based on wordnet and modified it to be used in Wikipedia, such as path-length measure [8], but in general the results are similar. However, Gabrilovich and Markovitch (2007) [5] propose a new approach with Explicit Semantic Analysis (ESA) that achieve highly accurate results, this method has been extensively studied in many applications [9]. ESA use Wikipedia as a semantic interpreter and builds a weighted inverted vector that maps each term into a list of Wikipedia articles in which it appears, and computes the similarity between vectors generated from two terms or texts.…”