Question Classification is the very first and pivotal step in Question Answering(QA) systems. It maps a given question into a predefined class . Classification of Consumer Health Questions contributes to answering medical queries of non-expert users. It plays a principal role in the performance of Consumer Health Question Answering systems . There are several approaches to the classification of questions. However, they do not consider the nature of Consumer Health Questions. In this study, we perform experiments focusing on the question representation methods to classify Consumer Health Questions accurately. These experiments provide an intuition of practical considerations for Consumer Health Question classification. The questions were represented through certain manually-designed features, word embedding , sentence embedding, and finetuning-based techniques. We create question classifiers that involve designing models for representing and categorizing questions. Based on the result, the fine-tuned Bidirectional Encoder Representations from Transformers(BERT)-large-based model with an accuracy of 86.00% on Genetic and Rare Diseases (GARD) and 80.40% on Yahoo! Answers-based datasets outperform previous works. In addition, the method based on A Robustly Optimized BERT Pretraining Approach(RoBERTa)-large model with 86.20% accuracy surpasses fine-tuned BERT-large model on the GARD dataset. Examining the outcomes of the models gives insight into decisive considerations for representing and classifying Consumer Health Questions.

We present a syntax-based reordering model (RM) for hierarchical phrase-based statistical machine translation (HPB-SMT) enriched with semantic features. Our model brings a number of novel contributions: (i) while the previous dependency-based RM is limited to the reordering of head and dependant constituent pairs, we also model the reordering of pairs of dependants; (ii) Our model is enriched with semantic features (Wordnet synsets) in order to allow the reordering model to generalize to pairs not seen in training but with equivalent meaning. (iii) We evaluate our model on two language directions: English-to-Farsi and English-to-Turkish. These language pairs are particularly challenging due to the free word order, rich morphology and lack of resources of the target languages. We evaluate our RM both intrinsically (accuracy of the RM classifier) and extrinsically (MT). Our best configuration outperforms the baseline classifier by 5–29% on pairs of dependants and by 12–30% on head and dependant pairs while the improvement on MT ranges between 1.6% and 5.5% relative in terms of BLEU depending on language pair and domain. We also analyze the value of the feature weights to obtain further insights on the impact of the reordering-related features in the HPB-SMT model. We observe that the features of our RM are assigned significant weights and that our features are complementary to the reordering feature included by default in the HPB-SMT model.

The rdfs:seeAlso predicate plays an important role in linking web resources in semantic web. Based on the W3C definition, it shows that the object resource provide additional information about the subject resource. Since providing additional information can take various forms, the definition is generic. In the other words, the rdfs:seeAlso link can present different meanings to the users and it can represents different kind of patterns and relationships between web resources. These patterns are unknown and have to be specified to help organizations, and individuals to interlink, and publish their datasets on Web of Data using the rdfs:seeAlso link. In this paper, we investigate to the traditional usages of seealso and then present a methodology to specify the patterns of rdfs:seeAlso usages in Semantic Web. The results of our investigation show that the discovered patterns constitute a significant portion of rdfs:seeAlso usages in Web of Data.

Similarity estimation between interconnected objects appears in many real-world applications and many domain-related measures have been proposed. This work proposes a new perspective on specifying the similarity between resources in linked data, and