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We are trying to predict problems in various ways and propose solutions to make it an exciting and rewarding safe and enjoyable experience.

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1 AnCora - Nom + AnCora - Verb Lecture POS 2 AnCora - Nom + AnCora - Verb POS + WSD 3 AnCora - Nom + AnCora - Verb Lemma POS + analysis 4 AnCora - Nom + AnCora - Verb POS + WSD + analysis 5 AnCora - Interpretation of POS 6 AnCora - Nom POS + WSD 7 Interpretation of AnCora - Nom POS + WSD + syntax analysis 9 - lemma POS + syntax analysis 10 - lemma POS + syntax analysis + SRL (NL) We designed classification tasks in various scenarios. Columns contain knowledge resources used in each scene (column 2), features to be used are extracted at the perception or lemma level (column 3), and in each case an NL processor is required.

This section describes the language resources used to build the final version of ADN Classifier (R3). I briefly introduced the AnCora-ES corpus and AnCora-Verb dictionary, introduced the details of AnCora-Nom dictionary, and got most of the functions to construct ADN classifier from there. AnCora-ES consists of 500,000 words (500 kw) of Spanish corpus 14 and text of newspaper annotated at different language level. Semantic verb class, named entity, and nominal meaning of WordNet) and phraseology (common reference) 15 (form of speech and vocabulary), grammar (component and function), meaning (language argument structure, theme) The corpus contains 10,678 complete resolution sentences. As described in Section 3, nomenclature is done using an intermediate classification model to automatically label expression types (23, 431). See step 5 in Figure 1.