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Li, Q.; Chen, Y.P.; Myaeng, S.-H.; Jin, Y.; Kang, B.-Y.: Concept unification of terms in different languages via web mining for Information Retrieval (2009)
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-
Fegley, B.D.; Torvik, V.I.: On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution (2012)
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- Abstract
- Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes' early poems from those of kindred poets and (b) diachronic: differentiate Hughes' early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation-based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (~10) achieved cross-validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes' early poetry.
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Ye, Z.; He, B.; Wang, L.; Luo, T.: Utilizing term proximity for blog post retrieval (2013)
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Malo, P.; Sinha, A.; Korhonen, P.; Wallenius, J.; Takala, P.: Good debt or bad debt : detecting semantic orientations in economic texts (2014)
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- Abstract
- The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. Recent research has developed sophisticated financial polarity lexicons for investigating how financial sentiments relate to future company performance. However, based on experience from fields that commonly analyze sentiment, it is well known that the overall semantic orientation of a sentence may differ from that of individual words. This article investigates how semantic orientations can be better detected in financial and economic news by accommodating the overall phrase-structure information and domain-specific use of language. Our three main contributions are the following: (a) a human-annotated finance phrase bank that can be used for training and evaluating alternative models; (b) a technique to enhance financial lexicons with attributes that help to identify expected direction of events that affect sentiment; and (c) a linearized phrase-structure model for detecting contextual semantic orientations in economic texts. The relevance of the newly added lexicon features and the benefit of using the proposed learning algorithm are demonstrated in a comparative study against general sentiment models as well as the popular word frequency models used in recent financial studies. The proposed framework is parsimonious and avoids the explosion in feature space caused by the use of conventional n-gram features.
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Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014)
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-
Reyes Ayala, B.; Knudson, R.; Chen, J.; Cao, G.; Wang, X.: Metadata records machine translation combining multi-engine outputs with limited parallel data (2018)
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-
Agarwal, B.; Ramampiaro, H.; Langseth, H.; Ruocco, M.: ¬A deep network model for paraphrase detection in short text messages (2018)
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-
Sienel, J.; Weiss, M.; Laube, M.: Sprachtechnologien für die Informationsgesellschaft des 21. Jahrhunderts (2000)
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- Date
- 26.12.2000 13:22:17
-
Fóris, A.: Network theory and terminology (2013)
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- Date
- 2. 9.2014 21:22:48
-
Kiss, T.: Anmerkungen zur scheinbaren Konkurrenz von numerischen und symbolischen Verfahren in der Computerlinguistik (2002)
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- Source
- Computerlinguistik -- Was geht, was kommt? = Computational linguistics : achievements and perspectives; Festschrift für Winfried Lenders. Hrsg.: G. Willée, B. Schröder & H.-C. Schmitz
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Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018)
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- Date
- 22. 1.2018 11:32:44
-
Schürmann, H.: Software scannt Radio- und Fernsehsendungen : Recherche in Nachrichtenarchiven erleichtert (2001)
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- Source
- Handelsblatt. Nr.79 vom 24.4.2001, S.22
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Melzer, C.: ¬Der Maschine anpassen : PC-Spracherkennung - Programme sind mittlerweile alltagsreif (2005)
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- Date
- 3. 5.1997 8:44:22
