1,721,038 research outputs found
Gramáticas locales de NooJ y pequeños mundos cognitivos: el concepto de fecha y duración
No full textFil: Monteleone, Mario. Università degli Studi di Salerno. Dipartimento di Scienze Politiche e della Comunicazione. Ital
NooJ Local Grammars for Endophora Resolution
No full textThe proceedings contain 21 papers. The special focus in this conference is on Automatic Processing of Natural-Language Electronic Texts with NooJ. The topics include: Treatments of the kabylian derived nominal verbs with nooj; addition of ipa transcription to the belarusian nooj module; recognizing diminutive and augmentative croatian nouns; inflectional and morphological variation of Arabic multi-word expressions; Quechua module for nooj multilingual linguistic resources for MT; nooj local grammars for innovative startup language; integration of a segmentation tool for Arabic corpora in nooj platform to build an automatic annotation tool; semi-automatic part-of-speech annotating for Belarusian dictionaries enrichment in nooj; recognition and extraction of Latin names of plants for matching common plant named entities; generating alerts from automatically-extracted tweets in standard Arabic; detection of verb frames with nooj; nooj local grammars for endophora resolution; paraphrases for the Italian communication predicates; endpoint for semantic knowledge and a decision-support tool of medicinal plants using nooj platform
NooJ grammars for Italian transformational analysis
No full textAs known, Transformational Grammar(TG)focuses on bidirectional relationshipsbetween sentences sharing the same lexical material, in some cases also thesamemeaning, but always differing in terms of formal structure and word distribution. We represent such relationships with the symbol “=” (equal to). For instance,we can connecta declarative sentence to its negative and/or passive forms.Similarly, we can connectcomplex sentences to therespectivesimple sentences,which make them up, such as sentences with reciprocal verbs and collective subjects,obtained through the coordination oftwo simple sentences.According to Maurice Gross[1] and Max Silberztein [2,3], examples of possible transformations arethose going from declarative sentences to Interrogatives, Pronominalization, Juxtapositions, or other processesproducing theso-called Mirror Transformations. In addition, two or more transformations can operate simultaneously on a declarative sentence
Morphosyntax and Semantics in the NooJ Italian Dictionary of Simple Words
No full textThe main topic of this paper is to describe how to transform effectively the “lexical matter” of a language (not only Italian) into a formal and taxonomic morphosyntactic classification of the simple words stored and tagged inside NooJ electronic dictionaries. The ultimate goal is to build electronic dictionaries for NooJ that can be used effectively in Natural Language Processing (NLP) and Automatic Textual Analysis (ATA). To achieve this task, we will start from the following assumption: nouns inflectioncodes may prove useful not only to describe morphological behaviours and features, but also to “predict” syntactic combinations inside sentences. This means that inside NooJ finite-state automata (FSA) and transducers (FST), nouns inflection codes can account for Lexicon-Grammar (LG) co-occurrence and selection restriction rules, thus also providing for several aspects of formal semantics (FS).The method we intend to outline here can therefore become a descriptive and applicative standard, reusable for all those languages in which word inflection has a value not only morphological (as for gender, number and possibly case) but also syntactic. As is known, such a formal descriptive approach is absent in paper dictionaries, in which there is a tendency to “flatten” the morphosyntactic description of words in favour of a list of words which uses rely heavily on the linguistic competence of readers and speakers
A Knowledge-Based CLIR Model for Specific Domain Collections
No full textThe effectiveness of Cross-language Information Retrieval (CLIR) applications clearly depends on the quality of translation, thus inaccurate or incorrect translations may cause serious problems in retrieving relevant information. Indeed, a very frequent source of mistranslations in specific domain texts is represented by multiword units (MWUs), and particularly, terminological word compounds: Processing and translating these forms of compound words is not a straightforward task since their morpho-syntactic and linguistic behaviour is quite complex and varied according to the various types and their translations are practically unpredictable. Our contribution presents an outline of the knowledge-based resources (dictionary, ontology and rules), developed by means of NooJ and used in the development of a knowledge-based CLIR system
Grammatical Disambiguation of Italian Words Using Part of the Speech of Words in the Context
No full textThe terminological tagging of the NooJ italian compound word dictionary
No full textIn this paper, and in relation to the construction of electronic dictionaries for NooJ, we will deal with the tagging of Italian compound words, and with how it differs from that of simple words as for methods, functions, and purposes. We will especially focus our attention on the tagging of technical-scientific compound words, demonstrating how this operation, in NooJ, represents a crucial tool for both information extraction and knowledge automatic management and representation. Furthermore, with the intention to producing a complete analysis, we will provide the definitions of simple word and compound word, from both a formal and a linguistic point of view. As for the linguistic examination, we will adopt two different approaches. For the first one, we will use the analytic methods of Zellig S. Harris, who first set out, in structuralist terms and in relation to English, the study of the composition of different morphemes in more complex linguistic units, hence also of word groups or phrases. For the second one, we will make extensive reference to the methodological framework of language formalization described by Maurice Gross' Lexicon-Grammar, as to its subsequent adaptations to the Italian language. Finally, as we will see, it will be of fundamental importance for us to differentiate the definitions that we will give here of compound words from the more generic and less precise one of multiword expressions (MWE). To justify this differentiation, we will provide not only formal indications, but also lexical, morphosyntactic and semantic ones
Predictive Maintenance with Linguistic Text Mining
No full textThe escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime.
In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy.
The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance.
This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings.
The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution.
The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience.
Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine.
This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency
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