Weighted Feature Typology Based on Semantic Feature Pr oduction Norms
نویسندگان
چکیده
منابع مشابه
Migrating Psycholinguistic Semantic Feature Norms into Linked Data in Linguistics
Semantic feature norms, originally utilized in the field of psycholinguistics as a tool for studying human semantic representation and computation, have recently attracted the attention of some NLP/IR researchers who wish to improve their task performances. However, currently available semantic feature norms are, by nature, not well-structured, making them difficult to integrate into existing r...
متن کاملPhishing website detection using weighted feature line embedding
The aim of phishing is tracing the users' s private information without their permission by designing a new website which mimics the trusted website. The specialists of information technology do not agree on a unique definition for the discriminative features that characterizes the phishing websites. Therefore, the number of reliable training samples in phishing detection problems is limited. M...
متن کاملOverlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
متن کاملSemantic Web Improved with the Weighted IDF Feature
The development of search engines is taking at a very fast rate. A lot of algorithms have been tried and tested. But, still the people are not getting precise results. Social networking sites are developing at tremendous rate and their growth has given birth to the new interesting problems. The social networking sites use semantic data to enhance the results. This provides us with a new perspec...
متن کاملFeature-Weighted Linear Stacking
Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the greatest reported gains have come from nonlinear procedures requiring significant tuning a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Temas em Psicologia
سال: 2017
ISSN: 2175-3652
DOI: 10.9788/tp2017.2-21en