Semantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.
To deal with the STS task, previous studies have resorted to various features (e.g. word overlap, synonym antonym), linguistic resources (e.g. WordNet and pre-trained word embeddings) and a wide assortment of learning algorithms (e.g. Support Vector Regression (SVR), regression functions and NNs). Among these works, several techniques extract multiple features of sentences and apply regression functions to estimate these similarity scores @cite_6 @cite_17 @cite_9 @cite_0 . analyzed distinctive word relations (e.g. synonyms, antonyms, and hyperonyms) with features based on counts of co-occurences with other words and similarities between captions of images. predicted the sentence similarity from syntactic relationship, distinctive content similitudes, length and string features. also utilized a regression algorithm to foresee the STS from different features (WordNet, word overlap, and so forth). Finally, combined relational syntactic structures with SVR.
@cite_0 @cite_9 @cite_6 @cite_17
2250387780 2155484602 2251919380 2106612368
Measuring semantic textual similarity (STS) is at the cornerstone of many NLP applications. Different from the majority of approaches, where a large number of pairwise similarity features are used to represent a text pair, our model features the following: (i) it directly encodes input texts into relational syntactic structures; (ii) relies on tree kernels to handle feature engineering automatically; (iii) combines both structural and feature vector representations in a single scoring model, i.e., in Support Vector Regression (SVR); and (iv) delivers significant improvement over the best STS systems. Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82 , ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems. This paper describes and analyzes our SemEval 2014 Task 1 system. Its features are based on distributional and denotational similarities; word alignment; negation; and hypernym hyponym, synonym, and antonym relations. This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference measures proposed in entailment judgement subtask, as well as common text similarity measures used in both subtasks. Then we exploited the same feature set to solve the both subtasks by considering them as a regression and a classification task respectively and performed a study of influence of different features. We achieved the first and the second rank for relatedness and entailment task respectively.