This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.
Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the user's previous purchases to web search engines sorting search hits based on the user's browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impact of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.
Enzymes active on components of lignocellulosic biomass are used for industrial applications ranging from food processing to biofuels production. These include a diverse array of glycoside hydrolases, carbohydrate esterases, polysaccharide lyases and oxidoreductases. Fungi are prolific producers of these enzymes, spurring fungal genome sequencing efforts to identify and catalogue the genes that encode them. To facilitate the functional annotation of these genes, biochemical data on over 800 fungal lignocellulose-degrading enzymes have been collected from the literature and organized into the searchable database, mycoCLAP (http://mycoclap.fungalgenomics.ca). First implemented in 2011, and updated as described here, mycoCLAP is capable of ranking search results according to closest biochemically characterized homologues: this improves the quality of the annotation, and significantly decreases the time required to annotate novel sequences. The database is freely available to the scientific community, as are the open source applications based on natural language processing developed to support the manual curation of mycoCLAP.Database URL: http://mycoclap.fungalgenomics.ca
In the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. Semantic frame structures are directly derived from word and basic concept sequences representing the users' utterances. A rulebased process provides a reference frame annotation of the speech training data. Then dynamic Bayesian networks are used to hypothesize frames from test data. The semantic frames used in this work are specialized on the task domain from the Berkeley FrameNet set.Experiments are reported on the French MEDIA dialog corpus. For all the data, the manual transcriptions and annotations at the word and concept levels are available. Tests are performed under 3 different conditions raising in difficulty wrt the errors in the word and concept sequence inputs. Three different stochastic models are compared and the results confirm the ability of the proposed probabilistic frameworks to carry out a reliable semantic frame annotation.Index Terms-spoken dialog system, spoken language understanding, semantic frames, semantic composition, dynamic Bayesian networks.
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