In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general-purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection.
Tandem LSTM-SVM approach for sentiment analysis
Cimino A;Dell'Orletta F
2016
Abstract
In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general-purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.anceserie | CEUR WORKSHOP PROCEEDINGS | - |
| dc.authority.anceserie | CEUR Workshop Proceedings | - |
| dc.authority.people | Cimino A | it |
| dc.authority.people | Dell'Orletta F | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/02/20 07:46:41 | - |
| dc.date.available | 2024/02/20 07:46:41 | - |
| dc.date.issued | 2016 | - |
| dc.description.abstracteng | In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general-purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection. | - |
| dc.description.affiliations | Istituto di Linguistica Computazionale Antonio Zampolli (ILC-CNR), Italia NLP Lab, Italy | - |
| dc.description.allpeople | Cimino, A; Dell'Orletta, F | - |
| dc.description.allpeopleoriginal | Cimino A.; Dell'Orletta F. | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 2 | - |
| dc.identifier.scopus | 2-s2.0-85009288926 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/333954 | - |
| dc.identifier.url | http://www.scopus.com/record/display.url?eid=2-s2.0-85009288926&origin=inward | - |
| dc.language.iso | eng | - |
| dc.relation.conferencedate | 7/12/2016 | - |
| dc.relation.conferencename | Evaluation of NLP and Speech Tools for Italian (EVALITA 1016) | - |
| dc.relation.conferenceplace | Napoli | - |
| dc.relation.volume | 1749 | - |
| dc.subject.keywords | sentiment analysis | - |
| dc.subject.keywords | nlp | - |
| dc.subject.keywords | neural network | - |
| dc.subject.singlekeyword | sentiment analysis | * |
| dc.subject.singlekeyword | nlp | * |
| dc.subject.singlekeyword | neural network | * |
| dc.title | Tandem LSTM-SVM approach for sentiment analysis | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.miur | 273 | - |
| dc.type.referee | Sì, ma tipo non specificato | - |
| dc.ugov.descaux1 | 366729 | - |
| iris.orcid.lastModifiedDate | 2024/04/04 13:17:34 | * |
| iris.orcid.lastModifiedMillisecond | 1712229454854 | * |
| iris.scopus.extIssued | 2016 | - |
| iris.scopus.extTitle | Tandem LSTM-SVM approach for sentiment analysis | - |
| iris.sitodocente.maxattempts | 2 | - |
| scopus.authority.anceserie | CEUR WORKSHOP PROCEEDINGS###1613-0073 | * |
| scopus.category | 1700 | * |
| scopus.contributor.affiliation | Italia NLP Lab | - |
| scopus.contributor.affiliation | Italia NLP Lab | - |
| scopus.contributor.afid | 60008941 | - |
| scopus.contributor.afid | 60008941 | - |
| scopus.contributor.auid | 57002803800 | - |
| scopus.contributor.auid | 57540567000 | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.dptid | 114087935 | - |
| scopus.contributor.dptid | 114087935 | - |
| scopus.contributor.name | Andrea | - |
| scopus.contributor.name | Felice | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale Antonio Zampolli (ILC-CNR); | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale Antonio Zampolli (ILC-CNR); | - |
| scopus.contributor.surname | Cimino | - |
| scopus.contributor.surname | Dell'Orletta | - |
| scopus.date.issued | 2016 | * |
| scopus.description.abstracteng | In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support Vector Machines is used for classification. SVMs combine the document embedding produced by the LSTM with a wide set of general-purpose features qualifying the lexical and grammatical structure of the text. We achieved the second best accuracy in Subjectivity Classification, the third position in Polarity Classification, the sixth position in Irony Detection. | * |
| scopus.description.allpeopleoriginal | Cimino A.; Dell'Orletta F. | * |
| scopus.differences | scopus.relation.conferencename | * |
| scopus.differences | scopus.authority.anceserie | * |
| scopus.differences | scopus.publisher.name | * |
| scopus.differences | scopus.relation.conferencedate | * |
| scopus.differences | scopus.identifier.doi | * |
| scopus.differences | scopus.relation.conferenceplace | * |
| scopus.document.type | cp | * |
| scopus.document.types | cp | * |
| scopus.identifier.doi | 10.4000/books.aaccademia.2003 | * |
| scopus.identifier.pui | 614074587 | * |
| scopus.identifier.scopus | 2-s2.0-85009288926 | * |
| scopus.journal.sourceid | 21100218356 | * |
| scopus.language.iso | eng | * |
| scopus.publisher.name | CEUR-WS | * |
| scopus.relation.conferencedate | 2016 | * |
| scopus.relation.conferencename | 3rd Italian Conference on Computational Linguistics, CLiC-it 2016 and 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, EVALITA 2016 | * |
| scopus.relation.conferenceplace | ita | * |
| scopus.relation.volume | 1749 | * |
| scopus.title | Tandem LSTM-SVM approach for sentiment analysis | * |
| scopus.titleeng | Tandem LSTM-SVM approach for sentiment analysis | * |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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