1,113 research outputs found
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
Uvid u automatsko izluÄivanje metaforiÄkih kolokacija
Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.Kolokacije su veÄ dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju Äine metaforiÄke kolokacije. Kod metaforiÄkih je kolokacija kod jedne od sastavnica doÅ”lo do semantiÄkoga pomaka, tj. jedna od sastavnica poprima preneseno znaÄenje. Glavni su ciljevi ovoga rada istražiti postojeÄu literaturu te dati sustavan pregled postojeÄih istraživanja na temu izluÄivanja kolokacija i postojeÄih metoda, mjera i resursa. PostojeÄa istraživanja opisana su i klasificirana prema razliÄitim pristupima (statistiÄki, hibridni i zasnovani na distribucijskoj semantici). TakoÄer su opisane razliÄite asocijativne mjere i postojeÄi naÄini procjene rezultata automatskoga izluÄivanja kolokacija. Metode, alati i resursi koji su koriÅ”teni u prethodnim istraživanjima, a mogli bi biti korisni za naÅ” buduÄi rad posebno su istaknuti. SteÄeni uvidi u postojeÄa istraživanja Äine prvi korak u razmatranju moguÄnosti razvijanja postupka za automatsko izluÄivanje metaforiÄkih kolokacija
Exploratory Search on Mobile Devices
The goal of this thesis is to provide a general framework (MobEx) for exploratory search especially on mobile devices. The central part is the design, implementation, and evaluation of several core modules for on-demand unsupervised information extraction well suited for exploratory search on mobile devices and creating the MobEx framework. These core processing elements, combined with a multitouch - able user interface specially designed for two families of mobile devices, i.e. smartphones and tablets, have been finally implemented in a research prototype. The initial information request, in form of a query topic description, is issued online by a user to the system. The system then retrieves web snippets by using standard search engines. These snippets are passed through a chain of NLP components which perform an ondemand or ad-hoc interactive Query Disambiguation, Named Entity Recognition, and Relation Extraction task. By on-demand or ad-hoc we mean the components are capable to perform their operations on an unrestricted open domain within special time constraints. The result of the whole process is a topic graph containing the detected associated topics as nodes and the extracted relation ships as labelled edges between the nodes. The Topic Graph is presented to the user in different ways depending on the size of the device she is using. Various evaluations have been conducted that help us to understand the potentials and limitations of the framework and the prototype
Thumbs up? Sentiment Classification using Machine Learning Techniques
We consider the problem of classifying documents not by topic, but by overall
sentiment, e.g., determining whether a review is positive or negative. Using
movie reviews as data, we find that standard machine learning techniques
definitively outperform human-produced baselines. However, the three machine
learning methods we employed (Naive Bayes, maximum entropy classification, and
support vector machines) do not perform as well on sentiment classification as
on traditional topic-based categorization. We conclude by examining factors
that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200
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