A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Enhanced Neurologic Concept Recognition using a Named Entity Recognition Model based on Transformers

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks to One based on Bidirectional Encoder Representation from Transformers. Models Were Evaluated for Accuracy of Text Span Identification on Three Text Corpora: Physician Notes from an Electronic Health Record, Case Histories from Neurologic Textbooks, and Clinical Synopses from an Online Database of Genetic Diseases. Both Models Performed Best on the Professionally-Written Clinical Synopses and Worst on the Physician-Written Clinical Notes. Both Models Performed Better When Signs and Symptoms Were Represented as Shorter Text Spans. Consistent with Prior Studies that Examined the Recognition of Diseases and Drugs, the Model based on Bidirectional Encoder Representations from Transformers Outperformed the Model based on Convolutional Neural Networks for Recognizing Signs and Symptoms. Recall for Signs and Symptoms Ranged from 59.5% to 82.0% and Precision Ranged from 61.7% to 80.4%. with Further Advances in NLP, Fully Automated Recognition of Signs and Symptoms in Electronic Health Records and the Medical Literature Should Be Feasible

A Review of Natural Language Processing Research

Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of ‘jumping curves’ from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding

Enhanced neurologic concept recognition using a named entity recognition model based on transformers

Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible

Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages supporting information, 7 pages reference

Thinking by metaphor, fast and slow: Deliberate Metaphor Theory offers a new model for metaphor and its comprehension

The immense increase in metaphor theory and research over the past decades is posing a threat of fragmentation to the field, which has been responded to by calls for new and more encompassing approaches to virtually all aspects metaphorical. This article argues that the opposite response may be more productive. By focusing on a different way of theorizing metaphor and its comprehension, existing theories and data can be re-ordered in an alternative and coherent way, which moreover breaks new grounds in tying up both with a general theory for all utterance comprehension as well as a general theory for all cognition as involving fast and slow thinking. The core of the new theory highlights the differentiation between deliberate and non-deliberate metaphor use, related to how people see the use of a metaphor as a metaphor in communication, that is, as a metaphor that counts as a metaphor between language users. It shows how this distinction can be employed to make sense of many insights about metaphor and its comprehension in innovative ways. The article outlines the foundations of the new theory and discusses how existing data, old and new, can be seen as supporting the new proposals

Sentiment analysis in SemEval: a review of sentiment identification approaches

ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions