• Contenu principal
  • Menu
OpenEdition Books
  • Accueil
  • Catalogue de 15898 livres
  • Éditeurs
  • Auteurs
  • Facebook
  • X
  • Partager
    • Facebook

    • X

    • Accueil
    • Catalogue de 15898 livres
    • Éditeurs
    • Auteurs
  • Ressources numériques en sciences humaines et sociales

    • OpenEdition
  • Nos plateformes

    • OpenEdition Books
    • OpenEdition Journals
    • Hypothèses
    • Calenda
  • Bibliothèques

    • OpenEdition Freemium
  • Suivez-nous

  • Lettre d’information
OpenEdition Search

Redirection vers OpenEdition Search.

À quel endroit ?
  • Accademia University Press
  • ›
  • Collana dell'Associazione Italiana di Li...
  • ›
  • EVALITA Evaluation of NLP and Speech Too...
  • ›
  • Track “Affect, Hate, and Stance”
  • ›
  • ATE_ABSITA: Aspect Term Extraction and A...
  • ›
  • SentNA @ ATE_ABSITA: Sentiment Analysis ...
  • Accademia University Press
  • Accademia University Press
    Accademia University Press
    Informations sur la couverture
    Table des matières
    Liens vers le livre
    Informations sur la couverture
    Table des matières
    Formats de lecture

    Plan

    Plan détaillé Texte intégral 1. Introduction 2. Our approach for Task 3 3. Preliminary results on Task 1 4. Conclusions Bibliographie Notes de bas de page Auteurs

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    Ce livre est recensé par

    Précédent Suivant
    Table des matières

    SentNA @ ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees with Lexical and Lexicon-based Features

    Francesco Mele, Antonio Sorgente et Giuseppe Vettigli

    p. 75-79

    Résumés

    This paper describes our submission to the tasks on Sentiment Analysis of ATE_ABSITA (Aspect Term Extraction and Aspect-Based Sentiment Analysis). In particular, we focused on Task 3 using an approach based on combining frequency of words with lexicon-based polarities and uses Boosted Trees to predict the sentiment score. This approach achieved a competitive error and, thanks to the interpretability of the building blocks, allows us to show the what elements are considered when making the prediction. We also joined Task 1 proposing a hybrid model that joins rule-based and machine learning methodologies in order to combine the advantages of both. The model proposed for Task 1 is only preliminary.

    Questo articolo descrive la nostra sottomissione ai tasks sulla Sentiment Analysis ATE_ABSITA (Aspect Term Extraction and Aspect-Based Sentiment Analysis). I nostri sforzi si sono concentrati sul Task 3 per il quale abbiamo adottato gli alberi di predizione (Boosted Trees) utilizzando come features di ingresso una combinazione basata sulla frequenza delle parole con la polarità derivate da un lessico. L’approccio raggiunge un errore competitivo e, grazie all’interpretabilità dei moduli intermedi, ci consente di analizzare in dettaglio gli elementi che caratterizzano maggiormente la fase di predizione. Una proposta è stata realizzata anche per il Task 1, dove abbiamo sviluppato un modello ibrido che combina un approcio basato su regole con tecniche Machine Learning. Il modello sviluppato per il Task 1 è solo in fase preliminare.

    Texte intégral Bibliographie Notes de bas de page Auteurs

    Texte intégral

    1. Introduction

    1User feedback has become essential for companies to improve their services and products. Nowadays, we can find user feedback in textual form as online reviews, posts on social media and so on. These resources can express overall opinions but also opinions about some specific details (aspects) of the subject. In this scenario, the tools provided by Sentiment Analysis are crucial to process user feedbacks, the ongoing research in this field is focused on creating models that are more and more accurate and that can also extract fine grained information for the data. As part of this research, the ATE_ABSITA tasks (Mattei et al. 2020)1, part of the EVALITA campaign (Basile et al. 2020), challenge the participants in extracting the aspects (Task 1), predict the sentiment towards each expect (Task 2) and also predict the overall sentiment expressed (Task 3) for a dataset containing reviews of items from an online shop.

    2It’s important to notice that the dataset released for the task is one of the few resources for the Italian language that has annotated aspects and sentiment at the same time. Others Italian resources that take into account sentiment with respect to aspects are (Sorgente, Vettigli, and Mele 2014) and (Croce et al. 2013). The first contains reviews of movies with 8 domain specific aspects and 5 different polarity values while the second contains opinions about wines considering 5 aspects and 3 possible polarity values.

    3This paper describes our approaches in solving task 1 and task 3. The approach for task 1 is still preliminary.

    4In the last decade top performing approaches to Sentiment Analysis have shifted from using classifiers on hand-crafted features, often based on lexicons (Zhu, Kiritchenko, and Mohammad 2014), to complex models based on deep Neural Networks and advanced word embeddings (Liu et al. 2020). While the latest models require special hardware and significant work to be trained, older approaches are built on top of well understood classification techniques that can be trained on commodity hardware which makes them easy to adapt for new applications. The approach proposed for Task 3 revisits the old fashioned style of doing Sentiment Analysis to see how it performs against more modern methodologies that are used in the competition.

    5Regarding Task 1 we follow the latest trend of exploiting linguistic patterns (Poria, Cambria, and Gelbukh 2016; Liu et al. 2015; Poria et al. 2014; Rana and Cheah 2019). What distinguishes our approach from others is that we use automatically generated patterns based on POS-Tags (Part of Speech-Tags) following the assumption that they are more robust to bad grammar compared to linguistic dependencies.

    6In Section 2 we will describe our approach for Task 3 and in Section 2.4 we will discuss the results. In Section 3 we will briefly discuss the preliminary model we build for Task 1 and its results.

    2. Our approach for Task 3

    7The idea behind our approach is to achieve competitive results using well known tools that can be used on commodity hardware. We build the features representing the text using n-grams and adding a set of characteristic annotated in SenticNet (Cambria et al. 2010). Given the large amount of features, we decided to use Boosted Trees as regression model given its ability to sub-sample the features dynamically. For textual preprocessing the libraries Spacy (Honnibal and Montani 2017) and Scikit-Learn (Pedregosa et al. 2011) were used. We chose XGboost (Chen and Guestrin 2016) as implementation of Boosted Trees for regression.

    2.1 Lexical features

    8Before extracting the lexical features we remove stop words (apart from words that can be used as negative adverbs) and lemmatized each word. Finally, we extract a set of n-grams from each review. We consider uni-grams, bi-grams and tri-grams at the same time.

    2.2 Lexicon-based features

    9To build the polarity features of our model, we have adopted SenticNet, a resource used for concept-level sentiment analysis. It contains a collection of concepts, including common-sense concepts, provided with values for polarity, attention, pleasantness and sensitivity. These are numerical features that are available for a subset of the words in each review. We take in account the average, the minimum and the maximum of all the values available in each review. We also consider the mood tags provided by SenticNet. They are sets of tags as #tristezza, #rabbia, #felicità2 attached to each word, we consider them as binary features.

    2.3 Regressor

    10Our final regressor is composed of 800 Decision Trees with a maximum depth of 4 layers. The model was trained using Gradient Boosting with a learning rate of 0.3. The final prediction is computed averaging the output of each tree. The rationale behind our choice is that we have a high number of features that are easy to use with tree based methods for specific cases, hence ensembling allows us to learn a set of shallow trees and each of them can work well for specific cases.

    2.4 Results and discussion

    11To build our model we initially focused on the training set using cross-validation to optimize the parameters achieving a root mean square error of 0.852 (the prediction target is on a scale from 1 to 5), we then tested the optimized model on the development set reaching an error of 0.805. We finally achieved an error of 0.795 on the final test set. The difference in the error across the different stages of validation suggests that the model is well trained as the error doesn’t increase when new data is presented. However, it also suggest that the estimation of the error has a wide confidence interval, the standard deviation estimated during cross validation is 0.049.

    Figure 1: Scatter plot that shows the annotated score against the predicted score on the development set

    Image 100000000000036900000361C4B9142A26DA5A3C.jpg

    12In Figure 1 we compare the scores predicted and the annotated score on the development set. The chart shows that the model has a tendency to over estimate the error, especially in cases annotated with a low score.

    13We will now examine two reviews for which our regressor has the highest error. This is the text of the first review:

    “si autospenge proprio quando si necessita di usarla contelecomando”3.

    14This review was annotated with a score of 2, but the score assigned by our system is 4.75. This highlights a tendency of the system to give higher scores in uncertain cases. In this specific case we have no adjectives and two typing mistakes that result in no information from the lexicon and most of the words being disregarded as rare by our preprocessing pipeline. This suggests that a special treatment is needed for these specific cases where the classifier has fewer elements to take a decision.

    15The text of the second review is:

    “Per questo prezzo c’è di meglio.. restituita.Gli accessori sono ottimi.”4.

    16This sentence was annotated with a score of 2, but the score assigned by our system is 3.36. We have again a case of over estimation of the score. This time the review has two contrasting sentences. A very negative one where the user states of having returned the item and a very positive one regarding the accessories. This ambivalence makes the review a borderline case for our model.

    17We attribute this tendency to overestimate the target to the fact that the model is optimized to minimize the root-mean-square error, this makes the model predict values closer to the average annotated score. While this is acceptable in an academic competition, it’s less than ideal in an industrial setting. One way to solve the overestimation problem, without changing the formulation of the error to minimize, would be to balance the data so to have a similar number of occurrences for each score. Sub-sampling the data is unpractical as it would reduce the sample size too drastically. This leaves open only the option to add more samples.

    Table 1: Important terms highlighted by the model. The column importance reports the importance score of the term while coverage is the cumulative sum of the importance scores

    term

    importance

    coverage %

    pessimo

    0.057123

    5.712323

    purtroppo

    0.038088

    9.521134

    rimborsare

    0.037871

    13.308205

    non consigliare

    0.033299

    16.638059

    purtroppo essere

    0.027965

    19.434580

    cattivo

    0.025690

    22.003609

    dispiacere

    0.024986

    24.502171

    pensare

    0.018631

    26.365243

    sconsigliare

    0.016331

    27.998360

    dopo

    0.016239

    29.622279

    non funzionare

    0.015425

    31.164802

    delusione

    0.015227

    32.687547

    non riconoscere

    0.014809

    34.168431

    restituire

    0.014615

    35.629894

    bruciare

    0.014250

    37.054852

    18In Table 1 we see the 15 terms most influential on the model. Here we note that most of the terms have a negative connotation. Interestingly, all the bi-grams in the list contain the word non (not). Taking in account that the terms reported in the table add up to 37% of the importance of all the features, this highlights the fact that the regressor puts particular attention in the prediction of reviews with a low score even if they are a minority.

    3. Preliminary results on Task 1

    19Task 1 asks to identify terms and phrases that contain an aspect of the customer review when it co-occurs with opinion words that bring information about the sentiment polarity.5

    20For this task we have designed a hybrid model that joins a rule-based approach with machine learning. The main idea is to identify a set of plausible aspects via some pre-defined rules, then use a classifier to filter out the wrong candidates. The rules are defined on POS-Tagging patterns. For example the review

    “Ottimo rasoio dal semplice utilizzo.”

    with annotated as aspect “semplice” matches the rule defined by the following pattern

    ADJ NOUN PROPN ADJ NOUN.

    21The bold tag indicates the position of the plausible aspect. We have defined a set of about 3000 rules. The rules have been discovered picking the most common POS-Tagging patterns that match the annotated aspects. In particular we have found the position of the aspects in the sentence and selected the POS of close words (three on each side) taking in account the punctuation.

    22Each aspect found can match one or more rules. The activation of each rule is used as binary feature for the final classifier. The final classifier is implemented using Logistic Regression (Hastie, Tibshirani, and Friedman 2001), its target is to predict if each candidate found by the rules is an actual candidate or a false positive.

    23This preliminary effort achieves a F1-score of 0.340, which is above the baseline (0.255) but below the average score of the submissions (0.504).

    4. Conclusions

    24The submission confirmed the effectiveness of using a simple approach to predict the sentiment score of customer reviews in Italian (Task 3). The approach consists in combining simple word embedding, specifically tri-grams, and a lexicon as SenticNet to build features for Boosted Trees. Our system achieved a competitive error which is lower than the baseline by 0.209 points and higher than the best model by 0.131 points. The error achieved above the average official score by 0.067 points (the estimates includes baseline models).

    25The submission also highlights that we were able to beat the baseline for Task 1 with a rudimentary approach. We will build upon this approach in our future work.

    Bibliographie

    Des DOI sont automatiquement ajoutés aux références bibliographiques par Bilbo, l’outil d’annotation bibliographique d’OpenEdition. Ces références bibliographiques peuvent être téléchargées dans les formats APA, Chicago et MLA.

    Format

    • APA
    • Chicago
    • MLA
    Basile, V., Croce, D., Maro, M., & Passaro, L. C. (Eds.). (2020). EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Accademia University Press. https://doi.org/10.4000/books.aaccademia.6732
    Chen, T., & Guestrin, C. (2016). XGBoost. Presented at the KDD ’16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785
    Basile, V., Croce, D., Maro, M., & Passaro, L. C. (Eds.). (2020). EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Accademia University Press. https://doi.org/10.4000/books.aaccademia.6732
    Sorgente, Antonio, Vettigli, Giuseppe, & Mele, Francesco. (2014). An Italian corpus for aspect based sentiment analysis of movie reviews. pisa university press. https://doi.org/10.12871/clicit2014167
    Basile, Valerio, Danilo Croce, Maria Maro, and Lucia C. Passaro, eds. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. []. Accademia University Press, 2020. doi:10.4000/books.aaccademia.6732.
    Chen, Tianqi, and Carlos Guestrin. “XGBoost”. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 13, 2016. doi:10.1145/2939672.2939785.
    Basile, Valerio, Danilo Croce, Maria Maro, and Lucia C. Passaro, eds. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. []. Accademia University Press, 2020. doi:10.4000/books.aaccademia.6732.
    Sorgente, Antonio, Vettigli, Giuseppe, and Mele, Francesco. “An Italian Corpus for Aspect Based Sentiment Analysis of Movie Reviews”. Proceedings of the First Italian Conference on Computational Linguistics CLiC-It 2014 and of the Fourth International Workshop EVALITA 2014 9-11 December 2014, Pisa. pisa university press, 2014. doi:10.12871/clicit2014167.
    Basile, Valerio, et al., editors. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. [], Accademia University Press, 2020. Crossref, https://doi.org/10.4000/books.aaccademia.6732.
    Chen, Tianqi, and Carlos Guestrin. “XGBoost”. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 13 Aug. 2016. Crossref, https://doi.org/10.1145/2939672.2939785.
    Basile, Valerio, et al., editors. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. [], Accademia University Press, 2020. Crossref, https://doi.org/10.4000/books.aaccademia.6732.
    Sorgente, Antonio, et al. “An Italian Corpus for Aspect Based Sentiment Analysis of Movie Reviews”. Proceedings of the First Italian Conference on Computational Linguistics CLiC-It 2014 and of the Fourth International Workshop EVALITA 2014 9-11 December 2014, Pisa, pisa university press, 2014. Crossref, https://doi.org/10.12871/clicit2014167.

    Cette bibliographie a été enrichie de toutes les références bibliographiques automatiquement générées par Bilbo en utilisant Crossref.

    [Basile et al.2020] Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. 2020. “EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian.” In Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (Evalita 2020), edited by Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. Online: CEUR.org.

    10.4000/books.aaccademia.6732 :

    [Cambria et al.2010] Erik Cambria, Robert Speer, Catherine Havasi, and Amir Hussain. 2010. “Senticnet: A Publicly Available Semantic Resource for Opinion Mining.” In AAAI Fall Symposium: Commonsense Knowledge. Vol. 10. Citeseer.

    [Chen and Guestrin2016] Tianqi Chen and Carlos Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System.” In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–94. KDD ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2939672.2939785.

    10.1145/2939672.2939785 :

    [Croce et al.2013] Danilo Croce, Francesco Garzoli, Marco Montesi, Diego De Cao, and Roberto Basili. 2013. “Enabling Advanced Business Intelligence in Divino.” In DART@AI*IA, 61–72.

    [de Mattei et al.2020] Lorenzo de Mattei, Graziella de Martino, Andrea Iovine, Alessio Miaschi, Marco Polignano, and Giulia Rambelli. 2020. “ATE_ABSITA@EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task.” In Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), edited by Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. Online: CEUR.org.

    10.4000/books.aaccademia.6732 :

    [Hastie et al.2001] Trevor Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2001. The Elements of Statistical Learning. Springer Series in Statistics. New York, NY, USA: Springer New York Inc.

    [Honnibal and Montani2017] Matthew Honnibal and Ines Montani. 2017. “spaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing.”

    [Liu et al.2015] Qian Liu, [Liu et al.2020] Jiaxiang Liu, Jiaxiang, Xuyi Chen, Shikun Feng, Shuohuan Wang, Xuan Ouyang, Yu Sun, Zhengjie Huang, and Weiyue Su. 2020. “Kk2018 at Semeval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification.” arXiv Preprint arXiv:2009.03673.

    [Liu et al.2015] Qian Liu, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2015. “Automated Rule Selection for Aspect Extraction in Opinion Mining.” In Twenty-Fourth International Joint Conference on Artificial Intelligence.

    [Pedregosa et al.2011] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.

    Poria et al.2016] Soujanya Poria, Erik Cambria, and Alexander Gelbukh. 2016. “Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network.” Knowledge-Based Systems 108: 42–49.

    [Poria et al.2014] Soujanya Poria, Erik Cambria, Lun-Wei Ku, Chen Gui, and Alexander Gelbukh. 2014. “A Rule-Based Approach to Aspect Extraction from Product Reviews.” In Proceedings of the Second Workshop on Natural Language Processing for Social Media (Socialnlp), 28–37.

    [Rana and Cheah2019] Toqir A Rana and Yu-N Cheah. 2019. “Sequential Patterns Rule-Based Approach for Opinion Target Extraction from Customer Reviews.” Journal of Information Science 45 (5): 643–55.

    [Sorgente et al.2014] Antonio Sorgente, Giuseppe Vettigli, and Francesco Mele. 2014. “An Italian Corpus for Aspect Based Sentiment Analysis of Movie Reviews.” In First Italian Conference on Computational Linguistics Clic-It.

    10.12871/clicit2014167 :

    [Zhu et al.2014] Xiaodan Zhu, Svetlana Kiritchenko, and Saif Mohammad. 2014. “Nrc-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets.” In Proceedings of the 8th International Workshop on Semantic Evaluation (Semeval 2014), 443–47.

    Notes de bas de page

    1 http://www.di.uniba.it/~swap/ate_absita/index.html

    2 In English: #sadness, #anger, #happiness

    3 In English: It turns off on its own when you need to use it with the remote control. (The original sentence contains a two typos.)

    4 In English: There’s a better choice for the same price.. I returned it.The accessories are great.

    5 Detailed description of the task at http://www.di.uniba.it/~swap/ate_absita/task.html

    Auteurs

    • Francesco Mele

      Institute of Applied Sciences and Intelligent Systems National Research Council – f.mele@isasi.cnr.it

    • Antonio Sorgente

      Institute of Applied Sciences and Intelligent Systems National Research Council – a.sorgente@isasi.cnr.it

    • Giuseppe Vettigli

      Centrica plc, Institute of Applied Sciences and Intelligent Systems (CNR) – giuseppe.vettigli@centrica.com

    Précédent Suivant
    Table des matières

    Creative Commons - Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International - CC BY-NC-ND 4.0

    Le texte seul est utilisable sous licence Creative Commons - Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International - CC BY-NC-ND 4.0. Les autres éléments (illustrations, fichiers annexes importés) sont « Tous droits réservés », sauf mention contraire.

    Voir plus de livres
    Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

    Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

    3-4 December 2015, Trento

    Cristina Bosco, Sara Tonelli et Fabio Massimo Zanzotto (dir.)

    2015

    Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

    Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

    5-6 December 2016, Napoli

    Anna Corazza, Simonetta Montemagni et Giovanni Semeraro (dir.)

    2016

    EVALITA. Evaluation of NLP and Speech Tools for Italian

    EVALITA. Evaluation of NLP and Speech Tools for Italian

    Proceedings of the Final Workshop 7 December 2016, Naples

    Pierpaolo Basile, Franco Cutugno, Malvina Nissim et al. (dir.)

    2016

    Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

    Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

    11-12 December 2017, Rome

    Roberto Basili, Malvina Nissim et Giorgio Satta (dir.)

    2017

    Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

    Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

    10-12 December 2018, Torino

    Elena Cabrio, Alessandro Mazzei et Fabio Tamburini (dir.)

    2018

    EVALITA Evaluation of NLP and Speech Tools for Italian

    EVALITA Evaluation of NLP and Speech Tools for Italian

    Proceedings of the Final Workshop 12-13 December 2018, Naples

    Tommaso Caselli, Nicole Novielli, Viviana Patti et al. (dir.)

    2018

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    Proceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop

    Valerio Basile, Danilo Croce, Maria Maro et al. (dir.)

    2020

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

    Bologna, Italy, March 1-3, 2021

    Felice Dell'Orletta, Johanna Monti et Fabio Tamburini (dir.)

    2020

    Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

    Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

    Milan, Italy, 26-28 January, 2022

    Elisabetta Fersini, Marco Passarotti et Viviana Patti (dir.)

    2022

    Voir plus de livres
    1 / 9
    Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

    Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

    3-4 December 2015, Trento

    Cristina Bosco, Sara Tonelli et Fabio Massimo Zanzotto (dir.)

    2015

    Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

    Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

    5-6 December 2016, Napoli

    Anna Corazza, Simonetta Montemagni et Giovanni Semeraro (dir.)

    2016

    EVALITA. Evaluation of NLP and Speech Tools for Italian

    EVALITA. Evaluation of NLP and Speech Tools for Italian

    Proceedings of the Final Workshop 7 December 2016, Naples

    Pierpaolo Basile, Franco Cutugno, Malvina Nissim et al. (dir.)

    2016

    Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

    Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

    11-12 December 2017, Rome

    Roberto Basili, Malvina Nissim et Giorgio Satta (dir.)

    2017

    Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

    Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

    10-12 December 2018, Torino

    Elena Cabrio, Alessandro Mazzei et Fabio Tamburini (dir.)

    2018

    EVALITA Evaluation of NLP and Speech Tools for Italian

    EVALITA Evaluation of NLP and Speech Tools for Italian

    Proceedings of the Final Workshop 12-13 December 2018, Naples

    Tommaso Caselli, Nicole Novielli, Viviana Patti et al. (dir.)

    2018

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    Proceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop

    Valerio Basile, Danilo Croce, Maria Maro et al. (dir.)

    2020

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

    Bologna, Italy, March 1-3, 2021

    Felice Dell'Orletta, Johanna Monti et Fabio Tamburini (dir.)

    2020

    Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

    Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

    Milan, Italy, 26-28 January, 2022

    Elisabetta Fersini, Marco Passarotti et Viviana Patti (dir.)

    2022

    Voir plus de chapitres

    The CHROME Manifesto: integrating multimodal data into Cultural Heritage Resources

    Francesco Cutugno, Felice Dell’Orletta, Isabella Poggi et al.

    Voir plus de chapitres

    The CHROME Manifesto: integrating multimodal data into Cultural Heritage Resources

    Francesco Cutugno, Felice Dell’Orletta, Isabella Poggi et al.

    Accès ouvert

    Accès ouvert

    ePub

    PDF

    PDF du chapitre

    1 http://www.di.uniba.it/~swap/ate_absita/index.html

    2 In English: #sadness, #anger, #happiness

    3 In English: It turns off on its own when you need to use it with the remote control. (The original sentence contains a two typos.)

    4 In English: There’s a better choice for the same price.. I returned it.The accessories are great.

    5 Detailed description of the task at http://www.di.uniba.it/~swap/ate_absita/task.html

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    X Facebook Email

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    Vous pouvez vous connecter à votre bibliothèque à l’adresse suivante : https://freemium.openedition.org/oebooks

    Suggérer l’acquisition à votre bibliothèque

    Si vous avez des questions, vous pouvez nous écrire à access[at]openedition.org

    EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

    Vérifiez si votre bibliothèque a déjà acquis ce livre : authentifiez-vous à OpenEdition Freemium for Books.

    Vous pouvez suggérer à votre bibliothèque d’acquérir un ou plusieurs livres publiés sur OpenEdition Books. N’hésitez pas à lui indiquer nos coordonnées : access[at]openedition.org

    Vous pouvez également nous indiquer, à l’aide du formulaire suivant, les coordonnées de votre bibliothèque afin que nous la contactions pour lui suggérer l’achat de ce livre. Les champs suivis de (*) sont obligatoires.

    Veuillez, s’il vous plaît, remplir tous les champs.

    La syntaxe de l’email est incorrecte.

    Référence numérique du chapitre

    Format

    Mele, F., Sorgente, A., & Vettigli, G. (2020). SentNA @ ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees with Lexical and Lexicon-based Features. In V. Basile, D. Croce, M. Maro, & L. C. Passaro (éds.), EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Torino: Accademia University Press. https://doi.org/10.4000/books.aaccademia.6874
    Mele, Francesco, Antonio Sorgente, et Giuseppe Vettigli. « SentNA @ ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees With Lexical and Lexicon-Based Features ». In EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020, édité par Valerio Basile, Danilo Croce, Maria Maro, et Lucia C. Passaro. Torino: Accademia University Press, 2020. doi:10.4000/books.aaccademia.6874.
    Mele, Francesco, et al. « SentNA @ ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees With Lexical and Lexicon-Based Features ». EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020, édité par Valerio Basile et al., Accademia University Press, 2020, https://doi.org/10.4000/books.aaccademia.6874.

    Référence numérique du livre

    Format

    Basile, V., Croce, D., Maro, M., & Passaro, L. C. (éds.). (2020). EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Torino: Accademia University Press. https://doi.org/10.4000/books.aaccademia.6732
    Basile, Valerio, Danilo Croce, Maria Maro, et Lucia C. Passaro, éd. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Torino: Accademia University Press, 2020. doi:10.4000/books.aaccademia.6732.
    Basile, Valerio, et al., éditeurs. EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020. Accademia University Press, 2020, https://doi.org/10.4000/books.aaccademia.6732.
    Compatible avec Zotero Zotero

    1 / 3

    Accademia University Press

    Accademia University Press

    • Plan du site
    • Se connecter

    Suivez-nous

    • Facebook
    • Flux RSS

    URL : http://www.aaccademia.it/

    Email : info@aaccademia.it

    Adresse :

    Accademia University Press

    Via Carlo Alberto 55

    I‐10123

    Torino

    Italia

    OpenEdition
    • Candidater à OpenEdition Books
    • Connaître le programme OpenEdition Freemium
    • Commander des livres
    • S’abonner à la lettre d’OpenEdition
    • CGU d’OpenEdition Books
    • Accessibilité : partiellement conforme
    • Données personnelles
    • Gestion des cookies
    • Système de signalement