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Computational power needs have greatly increased during the last years, and this is also the case in the Natural Language Processing (NLP) area, where thousands of documents must be processed, i.e., linguistically analyzed, in a reasonable time frame. These computing needs have implied a radical change in the computing architectures and big-scale text processing techniques used in NLP. In this paper, we present a scalable architecture for distributed language processing. The architecture uses Storm to combine diverse NLP modules into a processing chain, which carries out the linguistic analysis of documents. Scalability requires designing solutions that are able to run distributed programs in parallel and across large machine clusters. Using the architecture presented here, it is possible to integrate a set of third-party NLP modules into a unique processing chain which can be deployed onto a distributed environment, i.e., a cluster of machines, so allowing the language-processing modules run in parallel. No restrictions are placed a priori on the NLP modules apart of being able to consume and produce linguistic annotations following a given format. We show the feasibility of our approach by integrating two linguistic processing chains for English and Spanish. Moreover, we provide several scripts that allow building from scratch a whole distributed architecture that can be then easily installed and deployed onto a cluster of machines. The scripts and the NLP modules used in the paper are publicly available and distributed under free licenses. In the paper, we also describe a series of experiments carried out in the context of the NewsReader project with the goal of testing how the system behaves in different scenarios.
Emotional creativity is defined as the ability to feel and express emotions in a new, effective and authentic way. There are currently no Basque-language self-report instruments to provide valid and reliable measures of this construct. Thus, this paper describes the process of adapting and validating the Emotional Creativity Inventory (ECI) for the Basque-speaking population. The sample was comprised of 594 higher education students (388 women and 206 men) aged between 18 and 32 years old (Mage = 20.47; SD = 2.48). The Basque version of the ECI was administered along with the TMMS-23, NEO PI-R, and PANAS. The results of exploratory and confirmatory factor analyses on the Basque ECI corroborated the original scale’s three-factor structure (preparedness, novelty, and effectiveness/authenticity). Those dimensions showed acceptable indexes of internal consistency (α = .80, .83, and .83) and temporal stability (r = .70, .69, and .74). The study also provided some evidence of external validity (p < .05) based on the relationships found between emotional creativity and emotional intelligence, personality, affect, and sex. The Basque ECI can be regarded as a useful tool to evaluate perceived emotional creativity during the preparation and verification phases of the creative process.
The lack of methodological rigor is frequent in most of instruments developed to assess the knowledge of teachers regarding Attention Deficit Hyperactivity Disorder (ADHD). The aim of this study was to develop a questionnaire, namely Questionnaire for the evaluation of teachers’ knowledge of ADHD (MAE-TDAH), for measuring the level of knowledge about ADHD of infant and primary school teachers. A random sample of 526 teachers from 57 schools in the Autonomous Community of the Basque Country and Navarre was used for the analysis of the psychometric properties of the instrument. The participant teachers age range was between 22 and 65 (M = 42.59; SD = 10.89), and there were both generalist and specialized teachers. The measure showed a 4 factor structure (Etiology of ADHD, Symptoms/Diagnosis of ADHD, General information about ADHD and Treatment of ADHD) with adequate internal consistency (Omega values ranged between .83 and .91) and temporal stability indices (Spearman’s Rho correlation values ranged between .62 and .79). Furthermore, evidence of convergent and external validity was obtained. Results suggest that the MAE-TDAH is a valid and reliable measure when it comes to evaluating teachers’ level of knowledge of ADHD.
The design and construction of lexical resources is a critical issue in Natural Language Processing (NLP). Real-world NLP systems need large-scale lexica, which provide rich information about words and word senses at all levels: morphologic, syntactic, lexical semantics, etc., but the construction of lexical resources is a difficult and costly task. The last decade has been highly influenced by the notion of reusability, that is, the use of the information of existing lexical resources in constructing new ones. It is unrealistic, however, to expect that the great variety of available lexical information resources could be converted into a single and standard representation schema in the near future. The purpose of this article is to present the ELHISA system, a software architecture for the integration of heterogeneous lexical information. We address, from the point of view of the information integration area, the problem of querying very different existing lexical information sources using a unique and common query language. The integration in ELHISA is performed in a logical way, so that the lexical resources do not suffer any modification when integrating them into the system. ELHISA is primarily defined as a consultation system for accessing structured lexical information, and therefore it does not have the capability to modify or update the underlying information. For this purpose, a General Conceptual Model (GCM) for describing diverse lexical data has been conceived. The GCM establishes a fixed vocabulary describing objects in the lexical information domain, their attributes, and the relationships among them. To integrate the lexical resources into the federation, a Source Conceptual Model (SCM) is built on the top of each one, which represents the lexical objects concurring in each particular source. To answer the user queries, ELHISA must access the integrated resources, and, hence, it must translate the query expressed in GCM terms into queries formulated in terms of the SCM of each source. The relation between the GCM and the SCMs is explicitly described by means of mapping rules called Content Description Rules. Data integration at the extensional level is achieved by means of the data cleansing process, needed if we want to compare the data arriving from different sources. In this process, the object identification step is carried out. Based on this architecture, a prototype named ELHISA has been built, and five resources covering a broad scope have been integrated into it so far for testing purposes. The fact that such heterogeneous resources have been integrated with ease into the system shows, in the opinion of the authors, the suitability of the approach taken.
This paper focuses on the design methodology of the MultiLingual Dictionary-System
(MLDS), which is a human-oriented tool for assisting in the task of translating lexical
units, oriented to translators and conceived from studies carried out with translators. We
describe the model adopted for the representation of multilingual dictionary-knowledge. Such
a model allows an enriched exploitation of the lexical-semantic relations extracted from
dictionaries. In addition, MLDS is supplied with knowledge about the use of the dictionaries
in the process of lexical translation, which was elicitated by means of empirical methods
and specified in a formal language. The dictionary-knowledge along with the task-oriented
knowledge are used to offer the translator active, anticipative and intelligent assistance.
This paper discusses different issues in the construction and knowledge
representation of an
intelligent dictionary help system. The Intelligent Dictionary Help System
(IDHS) is conceived
as a monolingual (explanatory) dictionary system for human use (Artola
The fact that it is intended for people instead of automatic processing
distinguishes it from
other systems dealing with the acquisition of semantic knowledge from conventional
dictionaries. The system provides various access possibilities to the data,
allowing to deduce
implicit knowledge from the explicit dictionary information. IDHS deals
mechanisms analogous to those used by humans when they consult a
dictionary. User level
functionality of the system has been specified and a prototype has
been implemented (Agirre
et al., 1994a). A methodology for the extraction of semantic
knowledge from a conventional
dictionary is described. The method followed in the construction of the
hierarchies required by the parser (Alshawi, 1989) is based on an
empirical study carried out
on the structure of definition sentences. The results of its application
to a real dictionary has
shown that the parsing method is particularly suited to the analysis of
sentences, as it was the case of the source dictionary. As a result of
this process, the
characterization of the different lexical-semantic relations between
senses is established by
means of semantic rules (attached to the patterns); these rules are
used for the initial
construction of the Dictionary Knowledge Base (DKB). The representation
for the DKB (Agirre et al., 1994b) is basically a semantic
network of frames representing word
senses. After construction of the initial DKB, several enrichment
processes are performed on
the DKB to add new facts to it; these processes are based on the
exploitation of the properties
of lexical-semantic relations, and also on specially conceived
deduction mechanisms. The result
of the enrichment processes show the suitability of the representation
schema chosen to deduce
implicit knowledge. Erroneous deductions are mainly due to incorrect word
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