Titlebook: Artificial Intelligence and Natural Language; 6th Conference, AINL Andrey Filchenkov,Lidia Pivovarova,Jan Žižka Conference proceedings 2018

固定某物

https://doi.org/10.1007/978-90-368-3041-6vantage of both methods. This paper proposes to obtain low-level feature representation feeding frame-level descriptor sequences to a Long Short-Term Memory (LSTM) network, combine the outcome with the Principal Component Analysis (PCA) representation of utterance-level features, and make the final prediction with a logistic regression classifier.

Lumbar-Stenosis

stressors

泄露

Deep Learning for Acoustic Addressee Detection in Spoken Dialogue Systemslization to increase the training speed. A fully-connected neural network reaches an average recall of 0.78, a Long Short-Term Memory neural network shows an average recall of 0.65. Advantages and disadvantages of both architectures are provided for the particular task.

Crumple

Combined Feature Representation for Emotion Classification from Russian Speechvantage of both methods. This paper proposes to obtain low-level feature representation feeding frame-level descriptor sequences to a Long Short-Term Memory (LSTM) network, combine the outcome with the Principal Component Analysis (PCA) representation of utterance-level features, and make the final prediction with a logistic regression classifier.

biopsy

装入胶囊

Morpheme Level Word Embeddingriments. Firstly, we describe how to build morpheme extractor from prepared vocabularies. Our extractor reached 91% accuracy on the vocabularies of known morpheme segmentation. Secondly we show the way how it can be applied for NLP tasks, and then we discuss our results, pros and cons, and our future work.

CAPE

pericardium

Nomadic

Tanmay,Lakshmi,Vijay Kumar Soni,Adarsh KumarFacebook status updates to extract interpretable features that we then use to identify Facebook users with certain negative psychological traits (the so-called Dark Triad: narcissism, psychopathy, and Machiavellianism) and to find the themes that are most important to such individuals.