2 research outputs found
Synonym Detection Using Syntactic Dependency And Neural Embeddings
Recent advances on the Vector Space Model have significantly improved some
NLP applications such as neural machine translation and natural language
generation. Although word co-occurrences in context have been widely used in
counting-/predicting-based distributional models, the role of syntactic
dependencies in deriving distributional semantics has not yet been thoroughly
investigated. By comparing various Vector Space Models in detecting synonyms in
TOEFL, we systematically study the salience of syntactic dependencies in
accounting for distributional similarity. We separate syntactic dependencies
into different groups according to their various grammatical roles and then use
context-counting to construct their corresponding raw and SVD-compressed
matrices. Moreover, using the same training hyperparameters and corpora, we
study typical neural embeddings in the evaluation. We further study the
effectiveness of injecting human-compiled semantic knowledge into neural
embeddings on computing distributional similarity. Our results show that the
syntactically conditioned contexts can interpret lexical semantics better than
the unconditioned ones, whereas retrofitting neural embeddings with semantic
knowledge can significantly improve synonym detection
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others