By Alexander Gelbukh, Félix Castro Espinoza, Sofía N. Galicia-Haro (eds.)
The two-volume set LNAI 8856 and LNAI 8857 constitutes the court cases of the thirteenth Mexican foreign convention on man made Intelligence, MICAI 2014, held in Tuxtla, Mexico, in November 2014. the entire of 87 papers plus 1 invited speak offered in those complaints have been conscientiously reviewed and chosen from 348 submissions. the 1st quantity bargains with advances in human-inspired computing and its purposes. It comprises forty four papers established into seven sections: usual language processing, typical language processing purposes, opinion mining, sentiment research, and social community purposes, machine imaginative and prescient, picture processing, common sense, reasoning, and multi-agent structures, and clever tutoring platforms. the second one quantity bargains with advances in nature-inspired computation and desktop studying and comprises additionally forty four papers dependent into 8 sections: genetic and evolutionary algorithms, neural networks, computer studying, computer studying purposes to audio and textual content, info mining, fuzzy good judgment, robotics, making plans, and scheduling, and biomedical applications.
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Additional resources for Human-Inspired Computing and Its Applications: 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Tuxtla Gutiérrez, Mexico, November 16-22, 2014. Proceedings, Part I
The rest of the paper is organized as follows: the problem of authorship attribution is brieﬂy introduced in Section 2, the concept of complete syntactic n-grams is presented in Section 3, experimental results of the proposal are presented and compared with baseline in Section 4 and, ﬁnally, we draw conclusions in Section 5. 2 Authorship Attribution The problem of authorship attribution can be deﬁned as follows: identify, from a set of possible authors, the author or authors of a text, prior analysis of examples of their writing style.
If we count the longest number of chars of this definition, we obtain a slightly higher score. 75% of precision, which is the highest value we obtained. As we are attempting all cases (our coverage is 100%), the recall figures, and the F-measures are the same as the precision values reported in Table 2. Table 2. Results for the Longest Definition Algorithm (counting bags of words). (Coverage=100%). Spanish Lexical Sample, Senseval-3. 31% Method Most frequent sense from answers of Senseval-3 training corpus MFS: largest number of words per definition in Minidir MFS: largest number of chars per definition in Minidir MFS: largest number of content words per definition in Minidir MFS: largest number of chars of content words per def.
The most frequent sense has more synonyms, more examples, a more detailed definition, etc. 0. We assume that a long gloss has more probability of being the MFS for a word. 0 there are 144,307 different words (many of them correspond to several synsets, and one synset can be tagged with different words). 6%) correspond with the manually tagged MFS in WordNet. 1%) words that correspond with the “gold standard” provided by WordNet, including monosemous words in both cases. We will see how this affects results in the Senseval tests.