Name
Abstract | ||
|---|---|---|
In this paper we describe our participation in the STS Core subtask which is the determination of the monolingual semantic similarity between pair of sentences. In our participation we adapted state-ofthe-art approaches from related work applied on previous STS Core subtasks and run them on the 2016 data. We investigated the performance of single methods but also the combination of them. Our results show that Convolutional Neural Networks (CNN) are superior to both the Monolingual Word Alignment and the Word2Vec approaches. The combination of all the three methods performs slightly better than using CNN only. Our results also show that the performance of our systems varies between the datasets. |
Year | Venue | Field |
|---|---|---|
2016 | SemEval@NAACL-HLT | Semantic similarity,SemEval,Computer science,Convolutional neural network,Speech recognition,Artificial intelligence,Natural language processing,Word2vec,The arts,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmet Aker | 1 | 267 | 30.75 |
| Frédéric Blain | 2 | 18 | 5.94 |
| Andrés Duque | 3 | 0 | 1.35 |
| Marina Fomicheva | 4 | 3 | 2.92 |
| Jurica Seva | 5 | 0 | 2.03 |
| Kashif Shah | 6 | 103 | 11.69 |
| Daniel Beck | 7 | 103 | 15.12 |