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Titlebook: Computational Processing of the Portuguese Language; 7th International Wo Renata Vieira,Paulo Quaresma,Maria Carmelita Dias Conference proc

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樓主: 動(dòng)詞
41#
發(fā)表于 2025-3-28 15:30:12 | 只看該作者
Tools for Nominalization: An Alternative for Lexical Normalization term generation and matching in an information retrieval environment. We present tools that automatically perform nominalization for lexical normalization in Portuguese. Comparing the effects of three alternative strategies (stemming, lemmatizing, and our proposal: nominalization), we demonstrate t
42#
發(fā)表于 2025-3-28 20:20:10 | 只看該作者
A Framework for Integrating Natural Language Toolsonnected as a chain of filters, apply successive transformations to the data that flows through the system. Hence when integrating such tools, one may face problems that lead to information losses, such as: (i) tools discard information from their input which will be required by other tools further
43#
發(fā)表于 2025-3-28 22:58:25 | 只看該作者
Methods and Tools for Encoding the WordNet.Br Sentences, Concept Glosses, and Conceptual-Semantic Reent conceptual-semantic relations (hyponymy, co-hyponymy, meronymy, cause, and entailment) and the so-called cross-lingual conceptual-semantic relations between different wordnets. Accordingly, after contextualizing the project and outlining the current lexical database structure and statistics, it
44#
發(fā)表于 2025-3-29 06:33:33 | 只看該作者
45#
發(fā)表于 2025-3-29 09:46:11 | 只看該作者
46#
發(fā)表于 2025-3-29 13:41:04 | 只看該作者
A Set of NP-Extraction Rules for Portuguese: Defining, Learning and Pruning machine learned set of transformation rules that was manually reviewed. The noun phrases extracted by these transformations were given as input to another learner that synthesized rules for breaking up complex noun phrases into simpler ones. The results of these processes applied to a Brazilian Por
47#
發(fā)表于 2025-3-29 15:55:33 | 只看該作者
48#
發(fā)表于 2025-3-29 22:28:26 | 只看該作者
49#
發(fā)表于 2025-3-30 03:55:25 | 只看該作者
50#
發(fā)表于 2025-3-30 04:17:09 | 只看該作者
Supply Chain Management with SAP APO?artificial neural networks) is presented. Among other contributions that this work intends to bring to natural language processing research, the employment of more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expec
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