Contextualized Question Answering

The paper describes a system which enables accurate and easy-to-use contextualized question answering and it provides document overview functionalities. The possibility of asking natural language questions enables a friendly interaction for the user.The contextualization is achieved by using an ontology. The answers are provided based on a domain specific document collection of choice. The approach consists of several phases as follows: data preparation, data enhancement, data indexing and handling questions. Every module uses state of the art technologies that are shown to work in a complex pipeline to make available question answering on top of a given document repository with the context of ontologies, such as Cyc, ASFA and WordNet. The functioning of the proposed approach is demonstrated on English document collections on Aquatic Sciences and Fisheries — ASFA, using Cyc ontology, ASFA thesaurus as domain specific ontology and WordNet as general ontology. Experimental evaluation has shown that the usage of ontologies increases the number of answers retrieved by about 60%. However, the number of answers that are actually correct increases by only 40% when using ontologies

NewsMeSH: a new classifier designed to annotate health news with MeSH headings

Motivation In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. Methods We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. Results A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. Conclusions The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar

A Multilingual Benchmark to Capture Olfactory Situations over Time

We present a benchmark in six European languages containing manually annotated information about olfactory situations and events following a FrameNet-like approach. The documents selection covers ten domains of interest to cultural historians in the olfactory domain and includes texts published between 1620 to 1920, allowing a diachronic analysis of smell descriptions. With this work, we aim to foster the development of olfactory information extraction approaches as well as the analysis of changes in smell descriptions over time

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively