We examined the potential advantage of the lexical databases using subtitles and present SUBTLEX-PT, a new lexical database for 132,710 Portuguese words obtained from a 78 million corpus based on film and television series subtitles, offering word frequency and contextual diversity measures. Additionally we validated SUBTLEX-PT with a lexical decision study involving 1920 Portuguese words (and 1920 nonwords) with different lengths in letters (M = 6.89, SD = 2.10) and syllables (M = 2.99, SD = 0.94). Multiple regression analyses on latency and accuracy data were conducted to compare the proportion of variance explained by the Portuguese subtitle word frequency measures with that accounted by the recent written-word frequency database (Procura-PALavras; P-PAL; Soares, Iriarte, et al., 2014 ). As its international counterparts, SUBTLEX-PT explains approximately 15% more of the variance in the lexical decision performance of young adults than the P-PAL database. Moreover, in line with recent studies, contextual diversity accounted for approximately 2% more of the variance in participants' reading performance than the raw frequency counts obtained from subtitles. SUBTLEX-PT is freely available for research purposes (at http://p-pal.di.uminho.pt/about/databases ).

In this article, we present Procura-PALavras (P-PAL), a Web-based interface for a new European Portuguese (EP) lexical database. Based on a contemporary printed corpus of over 227 million words, P-PAL provides a broad range of word attributes and statistics, including several measures of word frequency (e.g., raw counts, per-million word frequency, logarithmic Zipf scale), morpho-syntactic information (e.g., parts of speech [PoSs], grammatical gender and number, dominant PoS, and frequency and relative frequency of the dominant PoS), as well as several lexical and sublexical orthographic (e.g., number of letters; consonant-vowel orthographic structure; density and frequency of orthographic neighbors; orthographic Levenshtein distance; orthographic uniqueness point; orthographic syllabification; and trigram, bigram, and letter type and token frequencies), and phonological measures (e.g., pronunciation, number of phonemes, stress, density and frequency of phonological neighbors, transposed and phonographic neighbors, syllabification, and biphone and phone type and token frequencies) for ~53,000 lemmatized and ~208,000 nonlemmatized EP word forms. To obtain these metrics, researchers can choose between two word queries in the application: (i) analyze words previously selected for specific attributes and/or lexical and sublexical characteristics, or (ii) generate word lists that meet word requirements defined by the user in the menu of analyses. For the measures it provides and the flexibility it allows, P-PAL will be a key resource to support research in all cognitive areas that use EP verbal stimuli. P-PAL is freely available at http://p-pal.di.uminho.pt/tools .

In this article, we introduce ESCOLEX, the first European Portuguese children's lexical database with grade-level-adjusted word frequency statistics. Computed from a 3.2-million-word corpus, ESCOLEX provides 48,381 word forms extracted from 171 elementary and middle school textbooks for 6- to 11-year-old children attending the first six grades in the Portuguese educational system. Like other children's grade-level databases (e.g., Carroll, Davies, & Richman, 1971; Corral, Ferrero, & Goikoetxea, Behavior Research Methods, 41, 1009-1017, 2009; Lété, Sprenger-Charolles, & Colé, Behavior Research Methods, Instruments, & Computers, 36, 156-166, 2004; Zeno, Ivens, Millard, Duvvuri, 1995), ESCOLEX provides four frequency indices for each grade: overall word frequency (F), index of dispersion across the selected textbooks (D), estimated frequency per million words (U), and standard frequency index (SFI). It also provides a new measure, contextual diversity (CD). In addition, the number of letters in the word and its part(s) of speech, number of syllables, syllable structure, and adult frequencies taken from P-PAL (a European Portuguese corpus-based lexical database; Soares, Comesaña, Iriarte, Almeida, Simões, Costa, …, Machado, 2010; Soares, Iriarte, Almeida, Simões, Costa, França, …, Comesaña, in press) are provided. ESCOLEX will be a useful tool both for researchers interested in language processing and development and for professionals in need of verbal materials adjusted to children's developmental stages. ESCOLEX can be downloaded along with this article or from http://p-pal.di.uminho.pt/about/databases .

In this paper, we examine methods to classify hate speech in social media. We aim to establish lexical baselines for this task by applying classification methods using a dataset annotated for this purpose. As features, our system uses Natural Language Processing (NLP) techniques in order to expand the original dataset with emotional information and provide it for machine learning classification. We obtain results of 80.56% accuracy in hate speech identification, which represents an increase of almost 100% from the original analysis used as a reference.

The Museum of the Person (Museu da Pessoa, MP) is a virtual museum with the purpose of exhibit life stories of common people. Its assets are composed of several interviews involving people whose stories we want to perpetuate. So the museum holds an heterogeneous collection of XML (eXtensible Markup Language) documents that constitute the working repository. The main idea is to extract automatically the information included in the repository in order to build the virtual museum's exhibition rooms. The goal of this paper is to describe an architectural approach to build a system that will create the virtual rooms from the XML repository to enable visitors to lookup individual life stories and also inter-cross information among them. We adopted the standard for museum ontologies CIDOC-CRM (CIDOC Conceptual Reference Model) refined with FOAF (Friend of a Friend) and DBpedia ontologies to represent OntoMP. That ontology is intended to allow a conceptual navigation over the available information. The approach here discussed is based on a TripleStore and uses SPARQL (SPARQL Protocol and RDF Query Language) to extract the information. Aiming at the extraction of meaningful information, we built a text filter that converts the interviews into a RDF triples file that reflects the assets described by the ontology.