Formation of professional ethics of future physicians-surgeons, using a “case-study” technology

Objective. To substantiate the prerequisites of moral behavior and to demonstrate the application procedures for a “case-study” technology while formation of professional ethics in the future physicians-surgeons. Materials and methods. The education-professional program for the specialists training in accordance to the 222 «Medicine» specialty, the education plans and programs of primary specialization for physician while internship in surgical specialties, and the situation exercises were analyzed. In the experiment 418 persons took part, who studied up, and 24 teachers of high medical schools. The investigation was conducted, using theoretical (bibliographic analysis), empyrical (observation, talks, questioning, expert opinions, testings, rangings) methods and procedures of mathematical statistics. Results. The indices of the ethics knowledge and capacities formed in the experimental group, the participants of which were studied in accordance to the “case-study” technology, have grown up, comparing with the paired indices of a control group. Conclusion. The «case-study” technology application consists of involvement of future physicians-surgeons in active analysis of various professional situations, gaining by them a certain experience of professional interrelations with colleagues, patients and their relatives,  subjects of interrelationship

A new benchmark dataset with production methodology for short text semantic similarity algorithms

This research presents a new benchmark dataset for evaluating Short Text Semantic Similarity (STSS) measurement algorithms and the methodology used for its creation. The power of the dataset is evaluated by using it to compare two established algorithms, STASIS and Latent Semantic Analysis. This dataset focuses on measures for use in Conversational Agents; other potential applications include email processing and data mining of social networks. Such applications involve integrating the STSS algorithm in a complex system, but STSS algorithms must be evaluated in their own right and compared with others for their effectiveness before systems integration. Semantic similarity is an artifact of human perception; therefore its evaluation is inherently empirical and requires benchmark datasets derived from human similarity ratings. The new dataset of 64 sentence pairs, STSS-131, has been designed to meet these requirements drawing on a range of resources from traditional grammar to cognitive neuroscience. The human ratings are obtained from a set of trials using new and improved experimental methods, with validated measures and statistics. The results illustrate the increased challenge and the potential longevity of the STSS-131 dataset as the Gold Standard for future STSS algorithm evaluation. © 2013 ACM 1550-4875/2013/12-ART17 15.00

Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers

We study the role of linguistic context in predicting quantifiers (‘few’, ‘all’). We collect crowdsourced data from human participants and test various models in a local (single-sentence) and a global context (multi-sentence) condition. Models significantly out-perform humans in the former setting and are only slightly better in the latter. While human performance improves with more linguistic context (especially on proportional quantifiers), model performance suffers. Models are very effective in exploiting lexical and morpho-syntactic patterns; humans are better at genuinely understanding the meaning of the (global) context

Exploring Resources for Lexical Chaining: A Comparison of Automated Semantic Relatedness Measures and Human Judgements

Cramer I, Wandmacher T, Waltinger U. Exploring Resources for Lexical Chaining: A Comparison of Automated Semantic Relatedness Measures and Human Judgements. In: Mehler A, Henning Lobin and Harald Lüngen and K-UK, Storrer A, Witt A, eds. Modeling, Learning and Processing of Text Technological Data Structures. Studies in Computational Intelligence. Vol 370. Berlin/New York: Springer; 2012: 377-396.In the past decade various semantic relatedness, similarity, and distance measures have been proposed which play a crucial role in many NLP-applications. Researchers compete for better algorithms (and resources to base the algorithms on), and often only few percentage points seem to suffice in order to prove a new measure (or resource) more accurate than an older one. However, it is still unclear which of them performs best under what conditions. In this work we therefore present a study comparing various relatedness measures. We evaluate them on the basis of a human judgment experiment and also examine several practical issues, such as run time and coverage. We show that the performance of all measures - as compared to human estimates - is still mediocre and argue that the definition of a shared task might bring us considerably closer to results of high quality