All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Generalisation in named entity recognition: A quantitative analysis

Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation

Improving Robustness and Scalability of Available Ner Systems

The focus of this research is to study and develop techniques to adapt existing NER resources to serve the needs of a broad range of organizations without expert NLP manpower. My methods emphasize usability, robustness and scalability of existing NER systems to ensure maximum functionality to a broad range of organizations. Usability is facilitated by ensuring that the methodologies are compatible with any available open-source NER tagger or data set, thus allowing organizations to choose resources that are easy to deploy and maintain and fit their requirements. One way of making use of available tagged data would be to aggregate a number of different tagged sets in an effort to increase the coverage of the NER system. Though, generally, more tagged data can mean a more robust NER model, extra data also introduces a significant amount of noise and complexity into the model as well. Because adding in additional training data to scale up an NER system presents a number of challenges in terms of scalability, this research aims to address these difficulties and provide a means for multiple available training sets to be aggregated while reducing noise, model complexity and training times. In an effort to maintain usability, increase robustness and improve scalability, I designed an approach to merge document clustering of the training data with open-source or available NER software packages and tagged data that can be easily acquired and implemented. Here, a tagged training set is clustered into smaller data sets, and models are then trained on these smaller clusters. This is designed not only to reduce noise by creating more focused models, but also to increase scalability and robustness. Document clustering is used extensively in information retrieval, but has never been used in conjunction with NER

Named Entity Recognition for Bacterial Type IV Secretion Systems

Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents