HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors

Abeed Sarker, Graciela Gonzalez


Abstract
We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different representations of it are concatenated, and the classification is performed using Support Vector Machines (SVM). Our system is particularly intended for use by non-experts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-score, our system obtained 8th position out of 39 submissions (F1-score: 0.632, average recall: 0.637, accuracy: 0.646).
Anthology ID:
S17-2105
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
640–643
Language:
URL:
https://www.aclweb.org/anthology/S17-2105
DOI:
10.18653/v1/S17-2105
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2105.pdf