UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
نویسندگان
چکیده
In this paper, we propose a deep neural network based natural language processing system for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by Highway Multilayer Perceptron to make predictions. Experimental results demonstrate that this approach can’t get better results on standard evaluation datasets.
منابع مشابه
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
In this paper we consider several approaches to predicting semantic textual similarity using word embeddings, as well as methods for forming embeddings for larger units of text. We compare these methods to several baselines, and find that none of them outperform the baselines. We then consider both a supervised and unsupervised approach to combining these methods which achieve modest improvemen...
متن کاملISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features
This paper describes our system developed for English Monolingual subtask (STS Core) of SemEval-2016 Task 1: “Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation”. We measure the similarity between two sentences using three different types of features, including word alignment-based similarity, sentence vector-based similarity and sentence constituent similar...
متن کاملRICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation
This paper describes our IR (Information Retrieval) based method for SemEval 2016 task 1, Semantic Textual Similarity (STS). The main feature of our approach is to extend a conventional IR-based scheme by incorporating word alignment information. This enables us to develop a more fine-grained similarity measurement. In the evaluation results, we have seen that the proposed method improves upon ...
متن کاملRobust semantic text similarity using LSA, machine learning, and linguistic resources
Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from se...
متن کاملUoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment
This paper presents the system submitted by University of Wolverhampton for SemEval-2014 task 1. We proposed a machine learning approach which is based on features extracted using Typed Dependencies, Paraphrasing, Machine Translation evaluation metrics, Quality Estimation metrics and Corpus Pattern Analysis. Our system performed satisfactorily and obtained 0.711 Pearson correlation for the sema...
متن کامل