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Faraj, N.: Analyse d'une methode d'indexation automatique basée sur une analyse syntaxique de texte (1996)
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- Canadian journal of information and library science. 21(1996) no.1, S.1-21
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Molto, M.: Improving full text search performance through textual analysis (1993)
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- Information processing and management. 29(1993) no.5, S.614-632
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Damerau, F.J.: Generating an evaluating domain-oriented multi-word terms from texts (1993)
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- Information processing and management. 29(1993) no.4, S.433-447
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Schuegraf, E.J.; Bommel, M.F.van: ¬An automatic document indexing system based on cooperating expert systems : design and development (1993)
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- Canadian journal of information and library science. 18(1993) no.2, S.32-50
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Malone, L.C.; Driscoll, J.R.; Pepe, J.W.: Modeling the performance of an automated keywording system (1991)
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- Information processing and management. 27(1991) nos.2/3, S.145-151
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Salton, G.; Allan, J.; Singhal, A.: Automatic text decomposition and structuring (1996)
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- Information processing and management. 32(1996) no.2, S.127-138
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Hlava, M.M.K.; Hainebach, R.: Machine aided indexing : European Parliament study and results (1996)
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- Imprint
- Medford, NJ : Information Today
-
Li, Z.: Research on dynamic morphological indexing (1998)
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- Source
- Journal of the China Society for Scientific and Technical Information. 17(1998) no.3, S.226-229
-
Lepsky, K.: Automatische Indexierung zur Erschließung deutschsprachiger Dokumente (1999)
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- nfd Information - Wissenschaft und Praxis. 50(1999) H.6, S.325-330
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Williams, R.V.: Hans Peter Luhn and Herbert M. Ohlman : their roles in the origins of keyword-in-context/permutation automatic indexing (2010)
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- Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.835-849
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Busch, D.: Domänenspezifische hybride automatische Indexierung von bibliographischen Metadaten (2019)
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- B.I.T.online. 22(2019) H.6, S.465-469
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Vilares, D.; Alonso, M.A.; Gómez-Rodríguez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages (2015)
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- Abstract
- Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product, or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focused on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic, and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1799-1816
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Yang, T.-H.; Hsieh, Y.-L.; Liu, S.-H.; Chang, Y.-C.; Hsu, W.-L.: ¬A flexible template generation and matching method with applications for publication reference metadata extraction (2021)
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- Abstract
- Conventional rule-based approaches use exact template matching to capture linguistic information and necessarily need to enumerate all variations. We propose a novel flexible template generation and matching scheme called the principle-based approach (PBA) based on sequence alignment, and employ it for reference metadata extraction (RME) to demonstrate its effectiveness. The main contributions of this research are threefold. First, we propose an automatic template generation that can capture prominent patterns using the dominating set algorithm. Second, we devise an alignment-based template-matching technique that uses a logistic regression model, which makes it more general and flexible than pure rule-based approaches. Last, we apply PBA to RME on extensive cross-domain corpora and demonstrate its robustness and generality. Experiments reveal that the same set of templates produced by the PBA framework not only deliver consistent performance on various unseen domains, but also surpass hand-crafted knowledge (templates). We use four independent journal style test sets and one conference style test set in the experiments. When compared to renowned machine learning methods, such as conditional random fields (CRF), as well as recent deep learning methods (i.e., bi-directional long short-term memory with a CRF layer, Bi-LSTM-CRF), PBA has the best performance for all datasets.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.1, S.32-45
-
Bonzi, S.: Representation of concepts in text : a comparison of within-document frequency, anaphora, and synonymy (1991)
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- Canadian journal of information science. 16(1991) no.3, S.21-31
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Driscoll, J.R.; Rajala, D.A.; Shaffer, W.H.: ¬The operation and performance of an artificially intelligent keywording system (1991)
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- Information processing and management. 27(1991) no.1, S.43-54
-
Cohen, J.D.: Highlights: language- and domain-independent automatic indexing terms for abstracting (1995)
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- Source
- Journal of the American Society for Information Science. 46(1995) no.3, S.162-174
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O'Kane, K.C.: Generating hierarchical document indices from common denominators in large document collections (1996)
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- Information processing and management. 32(1996) no.2, S.105-115
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Hlava, M.M.K.: Machine aided indexing (MAI) in a multilingual environment (1993)
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- Imprint
- Medford, NJ : Learned Information
-
Pulgarin, A.; Gil-Leiva, I.: Bibliometric analysis of the automatic indexing literature : 1956-2000 (2004)
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- Information processing and management. 40(2004) no.2, S.365-377
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Dolamic, L.; Savoy, J.: When stopword lists make the difference (2009)
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- Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.200-203
