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Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 1651–1700 of 1706 papers

TitleStatusHype
Improved Sentence-Level Arabic Dialect Classification—0
An Error Analysis Tool for Natural Language Processing and Applied Machine Learning—0
Feature Engineering for Knowledge Base Construction—0
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network—0
Robust Domain Adaptation for Relation Extraction via Clustering Consistency—0
Bayesian Kernel Methods for Natural Language Processing—0
Max-Margin Tensor Neural Network for Chinese Word Segmentation—0
Word-Based Dialog State Tracking with Recurrent Neural Networks—0
Linguistic Structured Sparsity in Text Categorization—0
Improving Citation Polarity Classification with Product Reviews—0
Learning Sentiment-Specific Word Embedding for Twitter Sentiment ClassificationCode0
How to Use less Features and Reach Better Performance in Author Gender Identification—0
Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing—0
Special Techniques for Constituent Parsing of Morphologically Rich Languages—0
Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization—0
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches—0
Automatic Feature Engineering for Answer Selection and Extraction—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Elephant: Sequence Labeling for Word and Sentence Segmentation—0
Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation—0
Deep Learning for Chinese Word Segmentation and POS Tagging—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Dual Training and Dual Prediction for Polarity Classification—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
Learning Adaptable Patterns for Passage Reranking—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
Parsing with Compositional Vector Grammars—0
Learning Semantic Textual Similarity with Structural Representations—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
Additive Neural Networks for Statistical Machine Translation—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Investigation of annotator's behaviour using eye-tracking data—0
Recurrent Convolutional Neural Networks for Discourse Compositionality—0
Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report—0
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis—0
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling—0
SZTE-NLP: Sentiment Detection on Twitter Messages—0
WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs—0
Sentiment Analysis of Political Tweets: Towards an Accurate Classifier—0
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent—0
Role of Morpho-Syntactic Features in Estonian Proficiency Classification—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations—0
FeatureForge: A Novel Tool for Visually Supported Feature Engineering and Corpus Revision—0
Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data—0
Joint Feature Selection in Distributed Stochastic Learning for Large-Scale Discriminative Training in SMT—0
Deep Learning for NLP (without Magic)—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
Rep\'erage des entit\'es nomm\'ees pour l'arabe : adaptation non-supervis\'ee et combinaison de syst\`emes (Named Entity Recognition for Arabic : Unsupervised adaptation and Systems combination) [in French]—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified