Estrategias de combinación de recursos lexicográficos en la adquisición de lenguas extranjeras (en contextos específicos)

En relativamente poco tiempo el mundo lexicográfico ha experimentado cambios radicales respecto a la producción, a la disponibilidad y al uso de los distintos recursos lexicográficos. Han surgido nuevos medios que, aunque no ti enen la búsqueda lexicográfica como primera función, bien pueden servir pa ra este objetivo: buscadores de internet, corpus de textos paralelos, foros electrónicos, medios social es, traductores automáticos, etc. En este contexto se plantean preguntas como qué papel desempeñan los distintos recursos lexicográficos en una búsqueda lexicográfica concreta, cuál es la combinación de ellos y cuál su cronología de uso. Este artículo profun diza en estas preguntas a base de un análisis del comportamiento lexicográfico en cinco leng uas extranjeras diferentes de 62 estudiantes de Administración de Empresas y, de esta manera, determina distintas estrategias de búsqueda

Od šupljoglavca do božice seksa: psovke u engleskim titlovima, njihove funkcije i prikaz modelom jezičnih povezanih podataka

Swear words represent an important social vehicle for human communication that beyond mere insults are conventionally used for social bonding and bantering among other functions. However, to the best of our knowledge, no systematic typology of swear word functions has been proposed. In this article, such a typology is proposed in a top-down manner drawing on literature as well as bottom-up by analysing a concrete corpus of real-world sufficiently vulgar dialogues. For this first case study, the analysis is limited to English and the analysis of subtitles of a single movie. We found that specific types of swear words, e.g. bodily functions, appear across functions with some preferences, e.g. body parts are particularly utilised in jocularity, criticism, and anger. Furthermore, resulting data are represented as linguistic linked data, extolling the virtues of this format for fine-grained linguistic analyses, e.g. filtering and visualising all swear words pertaining to a specific function.Psovke su važno društveno sredstvo za ljudsku komunikaciju koje se, osim za puke uvrede, konvencionalno upotrebljavaju i za društveno povezivanje i zadirkivanje. Međutim, koliko nam je poznato, dosad nije predložena nijedna sustavna tipologija funkcija psovki. U ovom se radu takva tipologija predlaže metodom odozgo prema dolje oslanjajući se na književnost, kao i metodom odozdo prema gore analizom konkretnoga korpusa provokativnih dijaloga iz stvarnoga svijeta. Predlaže se dvanaest funkcija psovki, a to su: šaljivo zbližavanje, zadirkivanje i šala, uvredljiva kritika, ljutnja i ismijavanje, neutralno procjenjivanje, zatim savjet, zahtjev, iznenađenje, bol i strah. Za ovo prvo istraživanje odabrali smo titlove britanskoga filma Dnevnik Bridget Jones, u kojem smo automatski detektirali 111 psovki primjenjujući filtar vulgarnosti. Priroda otkrivenih psovki varirala je od navođenja tjelesnih dijelova i njihovih funkcija do upućivanja na vjeru ili mentalne bolesti. Dijelovi tijela, na primjer, posebno su korišteni za izražavanje šaljivosti, kritičnosti i ljutnje u spomenutom kontekstu, npr. engl. bollocks. Kako bismo prikazali rezultate, stvorili smo dva LLOD izvora: konceptualni skup funkcija i leksikon psovki. Svakom značenju psovke dodijeljen je zaseban leksički unos u OntoLexu i povezan s definicijom i njezinom funkcijom. Kako bi se svaki unos opremio primjerom konteksta i učestalošću pojavljivanja, korišten je modul OntoLex-FrAC. Kako bismo povezali funkcije iz skupa pojmova s leksičkim unosom psovke, uvodimo novo svojstvo objekta. U budućnosti se planira ponovna primjena tipologije psovki u analizi prijevoda titlova te u istraživanju uočavanja promjene u funkciji ili prirodi psovki

Crowdsourcing Image Schemas

With their potential to map experiental structures from the sensorimotor to the abstract cognitive realm, image schemas are believed to provide an embodied grounding to our cognitive conceptual system, including natural language. Few empirical studies have evaluated humans’ intuitive understanding of image schemas or the coherence of image-schematic annotations of natural language. In this paper we present the results of a human-subjects study in which 100 participants annotate 12 simple English sentences with one or more image schemas. We find that human subjects recruited from a crowdsourcing platform can understand image schema descriptions and use them to perform annotations of texts, but also that in many cases multiple image schema annotations apply to the same simple sentence, a phenomenon we call image schema collocations. This study carries implications both for methodologies of future studies of image schemas, and for the inexpensive and efficient creation of large text corpora with image schema annotations

Towards Modeling Conceptual Dependency Primitives with Image Schema Logic

Conceptual Dependency (CD) primitives and Image Schemas (IS) share a common goal of grounding symbols of natural language in a representation that allows for automated semantic interpretation. Both seek to establish a connection between high-level conceptualizations in natural language and abstract cognitive building blocks. Some previous approaches have established a CD-IS correspondence. In this paper, we build on this correspondence in order to apply a logic designed for image schemas to selected CD primitives with the goal of formally taking account of the CD inventory. The logic draws from Region Connection Calculus (RCC-8), Qualitative Trajectory Calculus (QTC), Cardinal Directions and Linear Temporal Logic (LTL). One of the primary premises of CD is a minimalist approach to its inventory of primitives, that is, it seeks to express natural language contents in an abstract manner with as few primitives as possible. In a formal analysis of physical primitives of CD we found a potential reduction since some primitives can be expressed as special cases of others

The Racing Mind and the Path of Love: automatic extraction of image schematic triggers in knowledge graphs generated from natural language

Embodied Cognition and Cognitive Metaphors Theory take their origin from our use of language: sensorimotor triggers are disseminated in our daily communication, expression and commonsense knowledge. We propose, in this work, a first attempt of image-schematic triggers automatic extraction, starting from knowledge graphs automatically generated from natural language. The methodology proposed here is conceived as a modular addition integrated in the FRED tool, able to generate knowledge graphs from natural language, while it has its foundation in querying ImageSchemaNet, the Image Schematic layer developed on top of FrameNet and integrated in the Framester resource. This methodology allows the extraction of sensorimotor triggers from WordNet, VerbNet, MetaNet, BabelNet and many more

Acquiring Terminological Relations with Neural Models for Multilingual LLOD Resources

Specialized communication strongly benefits from the availability of structured and consistent domain-specific knowledge in LLOD language resources. Manually curating such language resources is cumbersome and time-intensive. Thus, automated approaches for extracting terms, concepts, and their interrelations are required. Recent advances in computational linguistics have enabled the training of highly multilingual neural language models, such as GPT-3 or XLM-R, that can successfully be adapted to various downstream tasks, from sentiment classification and text completion to information extraction. Furthermore, several approaches exist to extract and explore lexico-semantic relations by means of these language models, however, only few focus on curating, representing, and interchanging domain-specific language resources in the LLOD cloud

Image Schemas and Conceptual Dependency Primitives: A Comparison

A major challenge in natural language understanding research in artificial intelligence (AI) has been and still is the grounding of symbols in a representation that allows for rich semantic interpretation, inference, and deduction. Across cognitive linguistics and other disciplines, a number of principled methods for meaning representation of natural language have been proposed that aim to emulate capacities of human cognition. However, little cross-fertilization among those methods has taken place. A joint effort of human-level meaning representation from AI research and from cognitive linguistics holds the potential of contributing new insights to this profound challenge. To this end, this paper presents a first comparison of image schemas to an AI meaning representation system called Conceptual Dependency (CD). Restricting our study to the domain of physical and spatial conceptual primitives, we find connections and mappings from a set of action primitives in CD to a remarkably similar set of image schemas. We also discuss important implications of this connection, from formalizing image schemas to improving meaning representation systems in AI