Abstract | Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g. |
Abstract | In this paper, we propose a generative cross-lingual mixture model (CLMM) to leverage unlabeled bilingual parallel data. |
Cross-Lingual Mixture Model for Sentiment Classification | In this section we present the cross-lingual mixture model (CLMM) for sentiment classification. |
Cross-Lingual Mixture Model for Sentiment Classification | We first formalize the task of cross-lingual sentiment classification. |
Introduction | In this paper we propose a cross-lingual mixture model (CLMM) for cross-lingual sentiment classification. |
Introduction | Besides, CLMM can improve the accuracy of cross-lingual sentiment classification consistently regardless of whether labeled data in the target language are present or not. |
Introduction | This paper makes two contributions: (1) we propose a model to effectively leverage large bilingual parallel data for improving vocabulary coverage; and (2) the proposed model is applicable in both settings of cross-lingual sentiment classification, irrespective of the availability of labeled data in the target language. |
Related Work | In this section, we present a brief review of the related work on monolingual sentiment classification and cross-lingual sentiment classification. |
Related Work | 2.2 Cross-Lingual Sentiment Classification |
Related Work | Cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e. g. Chinese) with labeled data in the source |
Abstract | Using a combination of lexical, syntactic, and semantic features to train a cross-lingual textual entailment system, we report promising results on different datasets. |
Conclusion | Our results in different cross-lingual settings prove the feasibility of the approach, with significant state-of-the-art improvements also on RTE-derived data. |
Experiments and results | 2Recently, a new dataset including “Unknown” pairs has been used in the “Cross-Lingual Textual Entailment for Content Synchronization” task at SemEval—2012 (Negri et al., 2012). |
Experiments and results | (3-way) demonstrates the effectiveness of our approach to capture meaning equivalence and information disparity in cross-lingual texts. |
Experiments and results | Cross-lingual models also significantly outperform pivoting methods. |
Introduction | In this paper we set such problem as an application-oriented, cross-lingual variant of the Textual Entailment (TE) recognition task (Dagan and Glickman, 2004). |
Introduction | (a) Experiments with multidirectional cross-lingual textual entailment. |
Introduction | So far, cross-lingual |
Abstract | To build a relation extractor without significant annotation effort, we can exploit cross-lingual annotation projection, which leverages parallel corpora as external resources for supervision. |
Conclusions | Experimental results show that our graph-based projection helped to improve the performance of the cross-lingual annotation projection of the semantic relations, and our system outperforms the other systems, which incorporate monolingual external resources. |
Cross-lingual Annotation Projection for Relation Extraction | Cross-lingual annotation projection intends to learn an extractor ft for good performance without significant effort toward building resources for a resource-poor target language L5. |
Cross-lingual Annotation Projection for Relation Extraction | Early studies in cross-lingual annotation projection were accomplished for various natural language processing tasks (Yarowsky and Ngai, 2001; Yarowsky et al., 2001; Hwa et al., 2005; Zitouni and Florian, 2008; Pado and Lapata, 2009). |
Introduction | To obtain training examples in the resource-poor target language, this approach exploited a cross-lingual annotation projection by propagating annotations that were generated by a relation extraction system in a resource-rich source language. |