# Linked Data Triples Enhance Document Relevance Classification
## 8 Concluding Remarks  
In this work, we developed the document relevance NN-classifier with three independent models applied on text, topics, and named entities (NEs), and these features are merged to form as a single multi-model classifier, leading to a higher-fidelity predictor with increased trainable parameters, called the _Topic-Entity Model_. The key question became: could the inclusion of topic-entity triples improve performance or convergence speed of a relevance classifier? We show that it can. The importance of this finding is that it demonstrates that triples add latent semantic enrichment to the relevance/irrelevance NN-classifier. We call this the _Topic-Entity-Triple Model_.  
The results in all datasets indicate that adding our proposed semantic features improves the performance of the classifier when compared with the Glove-based bag-of-words model. The F-score of the semantic feature-enriched models on the Glove representations is improved over the baseline by 6%. GloVe representations on Energy Hub is on par with that of the computationally expensive and state-of-the-art sentence embeddings-based classifier. The performance of the state-of-the art InferSent and BERT encoders also show significant improvements using our proposed models over their respective baselines.  
Furthermore, the Energy-Hub dataset consists of news articles scraped from real-world web pages with huge amounts of noise in the form of advertising content, social media plugins, and other article recommendations, unlike Reuters, which is a clean benchmark dataset. Our observations, therefore, reveal that the proposed semantic features extracted from the raw, noisy text play a more significant role in separating the two classes of documents as compared with situations where there is noise-free text in documents.  
This paper proposes a novel way of testing the latent semantic value of triples (linked data) by experimenting with state-of-the-art NLP-techniques. Document text, LDA topics, and named entities act as core elements in the formation of triples that are added as features to train a deep neural network relevance classifier. We showed that the best performing classifier for document relevance is one that is formed by a complex network architecture with plain text and added semantic features that are constructed by connecting LDA topics, named entities, and topic-entity triples. This validates the hypothesis that topics, named entities, and triples (linked data) constructed from these NLP-elements enrich semantic features in determining document relevance/irrelevance, especially in the context of news-feed classification.  
Conceptualisation, D.N. and P.W.E.; methodology, D.N., P.W.E.[PERSON] B.O. and M.R.B.; software, D.N.; validation, P.W.E., B.O. and M.R.B.; writing-original draft preparation, D.N., P.W.E., writing-review and editing, P.W.E., B.O., M.R.B.; supervision, P.W.E. All authors have read and agreed to the published version of the manuscript.  
This research is funded by \"Minerals Council of Australia\".  
Not applicable[PERSON]  
Data Availability Statement: Data is included within the article.  
Conflicts of Interest: The authors declare no conflict of interest.