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- Paper.28_Review.0 hasContent "The paper presents a contribution on Aspect-based Sentiment Analysis, that is, sentiment analysis where the sentence may contains more opinions on a number of aspects, with potentially different polarity. The work is based/compares against the previous work of one of the authors. The previous approach, based on a given ontology exploited to improve the sentiment analysis task, is improved in this work through semi-automatic generation of the ontology based on external resources providing synsets. This work almost reaches the performance of the previous approach, but require less time to the user to construct the ontology due to the semi-automatic generation. The paper needs some extra work to improve the presentation, and it is difficult to read in some points. In the following some examples: On page 3 "finally the resulting ontology...", which ontology is it? Further, you are describing data (section 3) without describing your approach first. Regarding SemEval-2016 data, authors mention that has "a target "word, aspect, sentiment and sentiment score", but a map to terms used in Fig 1 would be beneficial (e.g. where is the sentiment score in the provided example?) "The term extraction method that is used has score based on domain pertinence (DP) and domain consensus (DC)" Is this part of a new contribution or you are just using results in [12]? In general it is not clear since the beginning the approach you are proposing, and whether it is a new one (which parts?) or whether you are applying an existing one to solve ABSA. The initial part of Sect 4.1, that is, the core contribution of the paper (Semi-automatic Ontology Learning), it does not go deep enough describing the approach. Also an example would be useful to the reader. Further, in this semi-automatic approach is not clear which part is performed automatically and which one requires human intervention (and to which extent). For instance, where the ontology in Fig.4 come from? On the other hand, subsection "Hierarchical Relations" seems to clearly describe a completely automatic step in building the ontology, described in a reproducible manner. Experiments: comparing only against [19] as baseline may perhaps be fair but not useful to understand the effectiveness of the approach w.r.t. very different approaches to ABSA. Further, although table 2 shows that the approach let the user save some time constructing the ontology, it is not clear whether this is true in different domains. Also, the metodology to get this result is not described clearly. Concluding, it is a potentially interesting and valuable work but at this stage may require some extra efforts to improve readability. Minor: moreover, moreover, furthermore... too many close repetitions in Section 3."".
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- Paper.28_Review.1 hasContent "The paper discusses a semi-automatic ontology builder approach for addressing the aspect-based sentiment analysis task. The paper is well written but the novelty is not completely clear since the number of approaches proposed in the literature about this topic is definitely huge. Anyway, I invite the authors to fix this work in two directions: related work and experiments. The related work section should include more recent contribution concerning the use of learning and argumentation techniques for aspect-based sentiment analysis. For example: - Mauro Dragoni, Célia da Costa Pereira, Andrea G. B. Tettamanzi, Serena Villata: Combining argumentation and aspect-based opinion mining: The SMACk system. AI Commun. 31(1): 75-95 (2018) Moreover, a discussion comparing the proposed contribution with respect to the papers cited within the related work section should be properly included where missing or expanded. The authors are invited to include in the related work section also works adopting semantic techniques for detecting aspects and opinions. An example is the following paper: - Marco Federici, Mauro Dragoni: A Knowledge-Based Approach for Aspect-Based Opinion Mining. SemWebEval@ESWC 2016: 141-152 While, concerning the evaluation, the authors are invited to test the algorithms also on the SemEval 2015 aspect-based benchmark. Moreover, results obtained by other SemEval campaign participants should be reported and discussed. ------------------ I thank the author for the effort in preparing the rebuttal. I confirm my score and I hope to see this paper accepted for ESWC."".
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- Paper.30 label "Cross-Domain Transfer Learning with Deep Neural Networks for Scientific Sentence Classification".
- Paper.30 title "Cross-Domain Transfer Learning with Deep Neural Networks for Scientific Sentence Classification".
- Paper.30 issued "2001-12-01T14:37:00.000Z".
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