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Found 9 papers, 0 papers with code
Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance
Our results suggest that further analyses of the features contributing to lyric-lyricist classification performance on the lowest lyricist-singer entropy group may improve the feature extraction task for lyricists.
Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization
In this paper, we integrated the estimator with the UNB.
Conservative Likelihood Ratio Estimator for Infrequent Data Slightly above a Frequency Threshold
A naive likelihood ratio (LR) estimation using the observed frequencies of events can overestimate LRs for infrequent data.
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites
We determined proper labels based for the words identified through pattern matching from product reviews to create the training data.
Product Information Browsing Support System Using Analytic Hierarchy Process
Large-scale e-commerce sites can collect and analyze a large number of user preferences and behaviors, and thus can recommend highly trusted products to users.
Feature Selective Likelihood Ratio Estimator for Low- and Zero-frequency N-grams
However, because this method deals with a large number of discrete values, the running time and memory usage for estimation are problematic.
Development of an Extractive Title Generation System Using Titles of Papers of Top Conferences for Intermediate English Students
The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students.
Unified Likelihood Ratio Estimation for High- to Zero-frequency N-grams
A naive estimation approach that uses only $N$-gram frequencies is sensitive to low-frequency (rare) $N$-grams and not applicable to zero-frequency (unobserved) $N$-grams; these are known as the low- and zero-frequency problems, respectively.
Journal Name Extraction from Japanese Scientific News Articles
We hypothesize that a journal name is likely to occur in a specific context.
