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  1. Paolillo, J.C.: Linguistics and the information sciences (2009) 0.01 
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    Date
    27. 8.2011 14:22:33
  2. Working with conceptual structures : contributions to ICCS 2000. 8th International Conference on Conceptual Structures: Logical, Linguistic, and Computational Issues. Darmstadt, August 14-18, 2000 (2000) 0.01 
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    Abstract
    The 8th International Conference on Conceptual Structures - Logical, Linguistic, and Computational Issues (ICCS 2000) brings together a wide range of researchers and practitioners working with conceptual structures. During the last few years, the ICCS conference series has considerably widened its scope on different kinds of conceptual structures, stimulating research across domain boundaries. We hope that this stimulation is further enhanced by ICCS 2000 joining the long tradition of conferences in Darmstadt with extensive, lively discussions. This volume consists of contributions presented at ICCS 2000, complementing the volume "Conceptual Structures: Logical, Linguistic, and Computational Issues" (B. Ganter, G.W. Mineau (Eds.), LNAI 1867, Springer, Berlin-Heidelberg 2000). It contains submissions reviewed by the program committee, and position papers. We wish to express our appreciation to all the authors of submitted papers, to the general chair, the program chair, the editorial board, the program committee, and to the additional reviewers for making ICCS 2000 a valuable contribution in the knowledge processing research field. Special thanks go to the local organizers for making the conference an enjoyable and inspiring event. We are grateful to Darmstadt University of Technology, the Ernst Schröder Center for Conceptual Knowledge Processing, the Center for Interdisciplinary Studies in Technology, the Deutsche Forschungsgemeinschaft, Land Hessen, and NaviCon GmbH for their generous support
    Content
    Concepts & Language: Knowledge organization by procedures of natural language processing. A case study using the method GABEK (J. Zelger, J. Gadner) - Computer aided narrative analysis using conceptual graphs (H. Schärfe, P. 0hrstrom) - Pragmatic representation of argumentative text: a challenge for the conceptual graph approach (H. Irandoust, B. Moulin) - Conceptual graphs as a knowledge representation core in a complex language learning environment (G. Angelova, A. Nenkova, S. Boycheva, T. Nikolov) - Conceptual Modeling and Ontologies: Relationships and actions in conceptual categories (Ch. Landauer, K.L. Bellman) - Concept approximations for formal concept analysis (J. Saquer, J.S. Deogun) - Faceted information representation (U. Priß) - Simple concept graphs with universal quantifiers (J. Tappe) - A framework for comparing methods for using or reusing multiple ontologies in an application (J. van ZyI, D. Corbett) - Designing task/method knowledge-based systems with conceptual graphs (M. Leclère, F.Trichet, Ch. Choquet) - A logical ontology (J. Farkas, J. Sarbo) - Algorithms and Tools: Fast concept analysis (Ch. Lindig) - A framework for conceptual graph unification (D. Corbett) - Visual CP representation of knowledge (H.D. Pfeiffer, R.T. Hartley) - Maximal isojoin for representing software textual specifications and detecting semantic anomalies (Th. Charnois) - Troika: using grids, lattices and graphs in knowledge acquisition (H.S. Delugach, B.E. Lampkin) - Open world theorem prover for conceptual graphs (J.E. Heaton, P. Kocura) - NetCare: a practical conceptual graphs software tool (S. Polovina, D. Strang) - CGWorld - a web based workbench for conceptual graphs management and applications (P. Dobrev, K. Toutanova) - Position papers: The edition project: Peirce's existential graphs (R. Mülller) - Mining association rules using formal concept analysis (N. Pasquier) - Contextual logic summary (R Wille) - Information channels and conceptual scaling (K.E. Wolff) - Spatial concepts - a rule exploration (S. Rudolph) - The TEXT-TO-ONTO learning environment (A. Mädche, St. Staab) - Controlling the semantics of metadata on audio-visual documents using ontologies (Th. Dechilly, B. Bachimont) - Building the ontological foundations of a terminology from natural language to conceptual graphs with Ribosome, a knowledge extraction system (Ch. Jacquelinet, A. Burgun) - CharGer: some lessons learned and new directions (H.S. Delugach) - Knowledge management using conceptual graphs (W.K. Pun)
  3. Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001) 0.01 
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  4. Ferret, O.; Grau, B.; Hurault-Plantet, M.; Illouz, G.; Jacquemin, C.; Monceaux, L.; Robba, I.; Vilnat, A.: How NLP can improve question answering (2002) 0.01 
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  5. Galitsky, B.: Can many agents answer questions better than one? (2005) 0.01 
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  6. 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) 0.01 
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  7. Perovsek, M.; Kranjca, J.; Erjaveca, T.; Cestnika, B.; Lavraca, N.: TextFlows : a visual programming platform for text mining and natural language processing (2016) 0.01 
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  8. Ghazzawi, N.; Robichaud, B.; Drouin, P.; Sadat, F.: Automatic extraction of specialized verbal units (2018) 0.01 
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  9. 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) 0.01 
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    Abstract
    Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. To uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (a) syntactic complexity, including measurements of sentence length and sentence complexity; and (b) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.
  10. Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023) 0.01 
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  11. Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation (1997) 0.01 
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    Date
    31. 7.1996 9:22:19
  12. Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.01 
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    Date
    16. 2.2000 14:22:39
  13. Rahmstorf, G.: Concept structures for large vocabularies (1998) 0.01 
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    Date
    30.12.2001 19:01:22
  14. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.01 
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    Date
    10. 1.2013 19:22:47
  15. Lawrie, D.; Mayfield, J.; McNamee, P.; Oard, P.W.: Cross-language person-entity linking from 20 languages (2015) 0.01 
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    Abstract
    The goal of entity linking is to associate references to an entity that is found in unstructured natural language content to an authoritative inventory of known entities. This article describes the construction of 6 test collections for cross-language person-entity linking that together span 22 languages. Fully automated components were used together with 2 crowdsourced validation stages to affordably generate ground-truth annotations with an accuracy comparable to that of a completely manual process. The resulting test collections each contain between 642 (Arabic) and 2,361 (Romanian) person references in non-English texts for which the correct resolution in English Wikipedia is known, plus a similar number of references for which no correct resolution into English Wikipedia is believed to exist. Fully automated cross-language person-name linking experiments with 20 non-English languages yielded a resolution accuracy of between 0.84 (Serbian) and 0.98 (Romanian), which compares favorably with previously reported cross-language entity linking results for Spanish.
  16. Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020) 0.01 
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  17. ¬The semantics of relationships : an interdisciplinary perspective (2002) 0.01 
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    Content
    Enthält die Beiträge: Pt.1: Types of relationships: CRUDE, D.A.: Hyponymy and its varieties; FELLBAUM, C.: On the semantics of troponymy; PRIBBENOW, S.: Meronymic relationships: from classical mereology to complex part-whole relations; KHOO, C. u.a.: The many facets of cause-effect relation - Pt.2: Relationships in knowledge representation and reasoning: GREEN, R.: Internally-structured conceptual models in cognitive semantics; HOVY, E.: Comparing sets of semantic relations in ontologies; GUARINO, N., C. WELTY: Identity and subsumption; JOUIS; C.: Logic of relationships - Pt.3: Applications of relationships: EVENS, M.: Thesaural relations in information retrieval; KHOO, C., S.H. MYAENG: Identifying semantic relations in text for information retrieval and information extraction; McCRAY, A.T., O. BODENREICHER: A conceptual framework for the biiomedical domain; HETZLER, B.: Visual analysis and exploration of relationships
  18. Shaalan, K.; Raza, H.: NERA: Named Entity Recognition for Arabic (2009) 0.01 
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    Abstract
    Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, nonstandardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.
  19. 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) 0.01 
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  20. Fegley, B.D.; Torvik, V.I.: On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution (2012) 0.01 
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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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  • a 53
  • el 10
  • m 7
  • s 7
  • p 2
  • b 1
  • x 1
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