Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts

Social Network Analysis (SNA) of organizations can attract great interest from government agencies and scientists for its ability to boost translational research and accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, and demonstrating the utility of the results for researchers in the pharmaceutical and biotechnology industries or national funding agencies.Comment: This paper has been withdrawn; First International Workshop on Graph Techniques for Biomedical Networks in Conjunction with IEEE International Conference on Bioinformatics and Biomedicine, Washington D.C., USA, Nov. 1-4, 2009; http://www.public.asu.edu/~sjonnal3/home/papers/IEEE%20BIBM%202009.pd

A named entity recognition system for Dutch

We describe a Named Entity Recognition system for Dutch that combines gazetteers, hand-crafted rules, and machine learning on the basis of seed material. We used gazetteers and a corpus to construct training material for Ripper, a rule learner. Instead of using Ripper to train a complete system, we used many different runs of Ripper in order to derive rules which we then interpreted and implemented in our own, hand-crafted system. This speeded up the building of a hand-crafted system, and allowed us to use many different rule sets in order to improve performance. We discuss the advantages of using machine learning software as a toot in knowledge acquisition, and evaluate the resulting system for Dutch

Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant

The purpose of this contribution is to present a feature extractor that was designed as a part of a Named Entity Recognition (NER) system, which is to be used in a Robotic Process Automation application with a self-learning ability. The NER system has a screen of the user interface as its input, and tries to recognize and categorize all the named entities that can be located within this screen. The set of features that can be extracted from the input, is discussed in the article. The local context features appear to be very important in the considered problem. Experiments show that the entities are recognized with a rate that is satisfactory from the business perspective

DisBot: a portuguese disaster support dynamic knowledge chatbot

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.info:eu-repo/semantics/publishedVersio