Feature Engineering
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning
... a feature engineering method using lexical, n-gram and psycholin- guistic features, (ii) a shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, ...
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Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit
... In this work, we experiment with multiple deep learning models for compound type classifi- cation. Our extensive experiments include standard neural models comprising of Multi-Layer Perceptrons (MLP), Convolution Neural ...
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Analysis of Rhythmic Phrasing: Feature Engineering vs Representation Learning for Classifying Readout Poetry
... In the Parlando subcorpus, we find per average 37 lines, 18 lines with finite verbs, and 25 lines using a punctuation. In the Variable Foot subcorpus, the same distribution is 20, 10, and 11. This indicates that the ...
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Convolutional Neural Networks vs Convolution Kernels: Feature Engineering for Answer Sentence Reranking
... automatic feature engineering: Convolution Tree Ker- nels (CTKs) and Convolutional Neural Net- works (CNNs) for learning to rank answer sentences in a Question Answering (QA) set- ...
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Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report
... Our goal is to predict the first language (L1) of English essays’s authors with the help of the TOEFL11 corpus where L1, prompts (top- ics) and proficiency levels are provided. Thus we approach this task as a ...
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Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection
... Lina Wang, et al. developed a comprehensive approach of feature engineering by combining all three domain features including statistical features in time domain and obtained higher average accuracy [5]. ...
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Dependency Tree based SRL with Proper Pruning and Extensive Feature Engineering
... This paper proposes a dependency tree- based SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in la- ...
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A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
... combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, ...
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An experimental study on feature engineering and learning approaches for aggression detection in social media
... Abstract With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to ...
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Complex Word Identification: Convolutional Neural Network vs Feature Engineering
... We describe the systems of NLP-CIC team that participated in the Complex Word Iden- tification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for iden- tifying complex words in English and other ...
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Arabic POS Tagging: Don’t Abandon Feature Engineering Just Yet
... significant feature engineering with bi-LSTM neu- ral network with and without feature engineering and word embeddings. We experiment with tag- ging each clitic in context and with tagging all ...
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The impact of simple feature engineering in multilingual medical NER
... simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical ...of feature engineering can improve the baseline results ...
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A Survey on Data Preparation and Feature Engineering in Machine Learning
... The paper describes the various methodologies involved in data cleaning and feature engineering. The paper emphasizes the step by step format required to perform a feature engineering task on ...
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A non DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task
... We described our system for the universal depen- dency parsing task that relies heavily on feature engineering for each component in the pipeline. Our system achieves reasonable performance. An important ...
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Multilingual Dependency Learning: A Huge Feature Engineering Method to Semantic Dependency Parsing
... This paper describes our system about mul- tilingual semantic dependency parsing (SR- Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into ...
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Automatic Feature Engineering for Answer Selection and Extraction
... Different from previous approaches that use tree- edit information derived from syntactic trees, our kernel-based learning approach also use tree struc- tures but with rather different learning methods, i.e., SVMs and ...
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Combination of Feature Engineering and Ranking Models for Paper-Author Identification in KDD Cup 2013
... This paper describes the winning solution of team National Taiwan University for track 1 of KDD Cup 2013. The track 1 in KDD Cup 2013 considers the paper-author identification problem, which is to identify whether a ...
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Feature Engineering for Second Language Acquisition Modeling
... Knowledge tracing is a vital element in person- alized and adaptive educational systems. In or- der to investigate the peculiarities of SLA and ex- plore the applicability of existing knowledge trac- ing techniques for ...
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NCSU SAS Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization
... For the constrained mode, dictionaries (includ- ing static mapping dictionary and similarity in- dex), classification feature calculation and classi- fier training are based on the same data set. It causes ...
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Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data
... unsupervised feature selection for CWS is based on frequent strings that are extracted automatically from unlabeled ...a feature similar to COS, called term-contributed boundary ...
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