Linked Data Triples Enhance Document Relevance Classification

Abstract

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.

Toward a Model to Evaluate Machine-Processing Quality in Scientific Documentation and Its Impact on Information Retrieval

“…The method's evaluation used the TREC 2004 and MSMARCO document collections. In their research, Nagumothu et al [19] demonstrated that Linked Data Triples in document relevance classification can significantly enhance the accuracy of classification in information retrieval systems based on deep learning techniques. To achieve this, they suggest constructing additional semantic features from natural language processing elements, such as named entity extraction, topic modeling, and linking these elements through Linked Data Triples.…”

A Multivariate Relevance Frequency Analysis Based Feature Selection for Classification of Short Text Data

“…With the entire text content as a vector space, each word in the text content will be a feature. The value of the features is provided by various term weighting techniques, such as the frequency of occurrence of the words or term frequency-inverted document frequency (TF-IDF) [10]. This method ignores the ordering of the words in the input text and is named the Bag of Words (BoW) approach [11].…”