Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

Abstract: We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine-tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.

A Taxonomy for Human-Robot Interaction

Abstract: This paper integrates research and ideas in the fields of Human-Computer Interaction and Robotics for the creation of a taxonomy for human-robot interaction. By drawing from multiple research fields, a more complete taxonomy is attained. Taxonomy categories include team composition (ratio of people to robots, types of robots), amount of required interaction, decision support provided for the user, and space-time location.

Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup.

Abstract: Motivation: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful. Results: We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new (‘blind’) articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.

Evolution of the Global Navigation SatelliteSystem (GNSS)

Abstract: The Global Navigation Satellite System (GNSS) is the worldwide set of satellite navigation constellations, civil aviation augmentations, and user equipment. This paper reviews the current status and future plans of the elements of GNSS as it pertains to civil aviation. The paper addresses the following satellite navigation systems: the U.S. Global Positioning System (GPS), Russian GLONASS, European Galileo, Chinese Compass, Japanese Quasi Zenith Satellite System, and Indian Regional Navigation Satellite System. The paper also describes aviation augmentations including aircraft-based, satellite-based, ground-based, and ground-based regional augmentation systems defined by the International Civil Aviation Organization. Lastly, this paper details typical user equipment configurations and civil aviation applications of GNSS including navigation, automatic dependent surveillance, terrain awareness warning systems, and timing.

Text mining for the biocuration workflow

Abstract: Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.