Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 24. Juni 2018)
1Gupta, P. ; Banchs, R.E. ; Rosso, P.: Continuous space models for CLIR.
In: Information processing and management. 53(2017) no.2, S.359-370.
Abstract: We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.
Inhalt: Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457316305982 [http://dx.doi.org/10.1016/j.ipm.2016.11.002].
Themenfeld: Multilinguale Probleme
2Panicheva, P. ; Cardiff, J. ; Rosso, P.: Identifying subjective statements in news titles using a personal sense annotation framework.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1411-1422.
Abstract: Subjective language contains information about private states. The goal of subjective language identification is to determine that a private state is expressed, without considering its polarity or specific emotion. A component of word meaning, "Personal Sense," has clear potential in the field of subjective language identification, as it reflects a meaning of words in terms of unique personal experience and carries personal characteristics. In this paper we investigate how Personal Sense can be harnessed for the purpose of identifying subjectivity in news titles. In the process, we develop a new Personal Sense annotation framework for annotating and classifying subjectivity, polarity, and emotion. The Personal Sense framework yields high performance in a fine-grained subsentence subjectivity classification. Our experiments demonstrate lexico-syntactic features to be useful for the identification of subjectivity indicators and the targets that receive the subjective Personal Sense.