This article introduces EsPal: a Web-accessible repository containing a comprehensive set of properties of Spanish words. EsPal is based on an extensible set of data sources, beginning with a 300 million token written database and a 460 million token subtitle database. Properties available include word frequency, orthographic structure and neighborhoods, phonological structure and neighborhoods, and subjective ratings such as imageability. Subword structure properties are also available in terms of bigrams and trigrams, biphones, and bisyllables. Lemma and part-of-speech information and their corresponding frequencies are also indexed. The website enables users either to upload a set of words to receive their properties or to receive a set of words matching constraints on the properties. The properties themselves are easily extensible and will be added over time as they become available. It is freely available from the following website: http:// www.bcbl.eu/databases/espal/. Keywords Word frequency . Subtitles . Word recognition . Corpus linguistics . PsycholinguisticsResearchers from a wide range of disciplines (e.g., neuroscience, artificial intelligence, psychology, linguistics, and education, among others) who work in the interdisciplinary area of language research (e.g., language acquisition, language processing, language learning, bilingualism, and computational linguistics) need quick and efficient access to information about specific properties of words. For example, word frequency is a dominant factor in accounting for visual word recognition speed as measured by lexical decision times (Forster & Chambers, 1973;Monsell, 1991) and eye fixation durations during reading (Rayner, 2009). Unsurprisingly, reading behavior as measured by, for example, lexical decision, naming, fixation times, and so on is affected by a wide range of other properties of words, including orthographic neighborhood

In this paper, we describe an approach to create a summary obfuscation corpus for the task of plagiarism detection. Our method is based on information from the Document Understanding Conferences related to years 2001 and 2006, for the English language. Overall, an unattributed summary used within someone else's document is considered a kind of plagiarism because the main author's ideas are still in a succinct form. In order to create the corpus, we use a Named Entity Recognizer (NER) to identify the entities within an original document, its associated summaries, and target documents. After, these entities, together with similar paragraphs in target documents, are used to make fake suspicious documents and plagiarized documents. The corpus was tested in plagiarism competition.

This article examines the mainstream categorical definition of coreference as "identity of reference." It argues that coreference is best handled when identity is treated as a continuum, ranging from full identity to non-identity, with room for near-identity relations to explain currently problematic cases. This middle ground is needed to account for those linguistic expressions in real text that stand in relations that are neither full coreference nor non-coreference, a situation that has led to contradictory treatment of cases in previous coreference annotation efforts. We discuss key issues for coreference such as conceptual categorization, individuation, criteria of identity, and the discourse model construct. We redefine coreference as a scalar relation between two (or more) linguistic expressions that refer to discourse entities considered to be at the same granularity level relevant to the linguistic and pragmatic context. We view coreference relations in terms of mental space theory and discuss a large number of real life examples that show near-identity at different degrees.

This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirec-tional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants , our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 tree-banks).

This article describes the enrichment of the AnCora corpora of Spanish and Catalan (400 k each) with coreference links between pronouns (including elliptical subjects and clitics), full noun phrases (including proper nouns), and discourse segments. The coding scheme distinguishes between identity links, predicative relations, and discourse deixis. Inter-annotator agreement on the link types is 85-89% above chance, and we provide an analysis of the sources of disagreement. The resulting corpora make it possible to train and test learning-based algorithms for automatic coreference resolution, as well as to carry out bottom-up linguistic descriptions of coreference relations as they occur in real data.