-
Engerer, V.: Indexierungstheorie für Linguisten : zu einigen natürlichsprachlichen Zügen in künstlichen Indexsprachen (2014)
0.09
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- Source
- Dialekte, Konzepte, Kontakte. Ergebnisse des Arbeitstreffens der Gesellschaft für Sprache und Sprachen, GeSuS e.V., 31. Mai - 1. Juni 2013 in Freiburg/Breisgau. Hrsg.: V. Schönenberger et al
-
Zadeh, B.Q.; Handschuh, S.: ¬The ACL RD-TEC : a dataset for benchmarking terminology extraction and classification in computational linguistics (2014)
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- Source
- Proceedings of the 4th International Workshop on Computational Terminology, Dublin, Ireland, August 23 2014. COLING 2014. Eds.: Patrick Drouin et al., Dublin, Ireland, 2014-08-23 [https://www.deri.ie/sites/default/files/publications/the-acl-rd-tec.pdf]
-
Korman, D.Z.; Mack, E.; Jett, J.; Renear, A.H.: Defining textual entailment (2018)
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- Abstract
- Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H?=?df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.
-
Rozinajová, V.; Macko, P.: Using natural language to search linked data (2017)
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- Source
- Semantic keyword-based search on structured data sources: COST Action IC1302. Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8-9, 2016, Revised Selected Papers. Eds.: A. Calì, A. et al
-
Nagy T., I.: Detecting multiword expressions and named entities in natural language texts (2014)
0.04
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- Abstract
- Multiword expressions (MWEs) are lexical items that can be decomposed into single words and display lexical, syntactic, semantic, pragmatic and/or statistical idiosyncrasy (Sag et al., 2002; Kim, 2008; Calzolari et al., 2002). The proper treatment of multiword expressions such as rock 'n' roll and make a decision is essential for many natural language processing (NLP) applications like information extraction and retrieval, terminology extraction and machine translation, and it is important to identify multiword expressions in context. For example, in machine translation we must know that MWEs form one semantic unit, hence their parts should not be translated separately. For this, multiword expressions should be identified first in the text to be translated. The chief aim of this thesis is to develop machine learning-based approaches for the automatic detection of different types of multiword expressions in English and Hungarian natural language texts. In our investigations, we pay attention to the characteristics of different types of multiword expressions such as nominal compounds, multiword named entities and light verb constructions, and we apply novel methods to identify MWEs in raw texts. In the thesis it will be demonstrated that nominal compounds and multiword amed entities may require a similar approach for their automatic detection as they behave in the same way from a linguistic point of view. Furthermore, it will be shown that the automatic detection of light verb constructions can be carried out using two effective machine learning-based approaches.
-
Kocijan, K.: Visualizing natural language resources (2015)
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- Source
- Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
-
Al-Shawakfa, E.; Al-Badarneh, A.; Shatnawi, S.; Al-Rabab'ah, K.; Bani-Ismail, B.: ¬A comparison study of some Arabic root finding algorithms (2010)
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-
Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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-
Becks, D.; Schulz, J.M.: Domänenübergreifende Phrasenextraktion mithilfe einer lexikonunabhängigen Analysekomponente (2010)
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- Source
- Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
-
Hmeidi, I.I.; Al-Shalabi, R.F.; Al-Taani, A.T.; Najadat, H.; Al-Hazaimeh, S.A.: ¬A novel approach to the extraction of roots from Arabic words using bigrams (2010)
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-
AL-Smadi, M.; Jaradat, Z.; AL-Ayyoub, M.; Jararweh, Y.: Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features (2017)
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-
Vasalou, A.; Gill, A.J.; Mazanderani, F.; Papoutsi, C.; Joinson, A.: Privacy dictionary : a new resource for the automated content analysis of privacy (2011)
0.01
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-
Ramisch, C.; Villavicencio, A.; Kordoni, V.: Introduction to the special issue on multiword expressions : from theory to practice and use (2013)
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-
Rosemblat, G.; Resnick, M.P.; Auston, I.; Shin, D.; Sneiderman, C.; Fizsman, M.; Rindflesch, T.C.: Extending SemRep to the public health domain (2013)
0.01
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-
Anguiano Peña, G.; Naumis Peña, C.: Method for selecting specialized terms from a general language corpus (2015)
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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.
-
Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016)
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-
Doval, Y.; Gómez-Rodríguez, C.: Comparing neural- and N-gram-based language models for word segmentation (2019)
0.01
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-
Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013)
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- Date
- 22. 3.2015 9:30:24
-
Vlachidis, A.; Binding, C.; Tudhope, D.; May, K.: Excavating grey literature : a case study on the rich indexing of archaeological documents via natural language-processing techniques and knowledge-based resources (2010)
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