Biomedical abbreviation recognition and resolution by PROSA-MED

The amount of abbreviations used in biomedical literature increases constantly. Despite the existence of acronym dictionaries, it is not viable to keep them updated with new creations. Thus, in the processing of biomedical texts, discovering and disambiguating acronyms and their expanded forms are essential aspects and this is the objective proposed by BARR task at IberEval 2017 Workshop. This paper presents our participation in this task. We propose five systems that deal with the problem in different ways. Three of the systems are atomic approaches, while two of them are combinations of the atomic systems. One of the systems clearly outperforms the others, both in the detection of entities (F-score of 0.749 in the test set) as well as identifying relations between short-long forms (F-score of 0.697 in the test set).Peer ReviewedPostprint (published version

A Chinese Named Entity Recognition System with Neural Networks

Named entity recognition (NER) is a typical sequential labeling problem that plays an important role in natural language processing (NLP) systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF) on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006

Deep Reference Mining From Scholarly Literature in the Arts and Humanities

We consider the task of reference mining: the detection, extraction and classification of references within the full text of scholarly publications. Reference mining brings forward specific challenges, such as the need to capture the morphology of highly abbreviated words and the dependence among the elements of a reference, both following codified reference styles. This task is particularly difficult, and little explored, with respect to the literature in the arts and humanities, where references are mostly given in footnotes. We apply a deep learning architecture for reference mining from the full text of scholarly publications. We explore and discuss three architectural components: word and character-level word embeddings, different prediction layers (Softmax and Conditional Random Fields) and multi-task over single-task learning. Our best model uses both pre-trained word embeddings and characters embeddings, and a BiLSTM-CRF architecture. We test our solution on a dataset of annotated references from the historiography on Venice and, using a linear-chain CRF classifier as a baseline, we show that this deep learning architecture improves by a considerable margin. Furthermore, multi-task learning performs almost on par with a single-task approach. We thus confirm that there are important gains to be had by adopting deep learning for the task of reference mining

An Application of Natural Language Processing for Triangulation of Cognitive Load Assessments in Third Level Education

Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities in third-level sessions under different topics and instructional designs. This research employs secondary, empirical and inductive methods to investigate Cognitive Load theory, instructional designs, Mental Workload foundations and measures and Natural Language Process Techniques. Then, NASA-TLX, Workload Profile and Relative Frequency Ratios are calculated. Finally, the relationship between NASA-TLX and Workload Profile and Relative Frequency Ratios is analysed using parametric and non-parametric statistical techniques. Results show that the relationship between Mental Workload and Relative Frequency Ratios of keywords, is only medium correlated, or not correlated at all. Furthermore, it has been found out that instructional designs based on the process of hearing and seeing, and the interaction between participants, can overcome other approaches such as those that make use of videos supported with images and text, or of a lecturer\u27s speech supported with slides