scholarly journals SimpleApprenant: a platform to improve French L2 learners’ knowledge of multiword expressions

Author(s):  
Amalia Todirascu ◽  
Marion Cargill

We present SimpleApprenant, a platform aiming to improve French L2 learners’ knowledge of Multi Word Expressions (MWEs). SimpleApprenant integrates an MWE database annotated with the Common European Framework of Reference for languages (CEFR) level and several Natural Language Processing (NLP) tools: a spelling checker, a parser, and a set of transformation rules. NLP tools and resources are used to build training and writing exercises to improve MWE knowledge and writing skills of French L2 learners. We present the user scenarios, the platform’s architecture, as well as the preliminary evaluation of its NLP tools.

2020 ◽  
Author(s):  
David DeFranza ◽  
Himanshu Mishra ◽  
Arul Mishra

Language provides an ever-present context for our cognitions and has the ability to shape them. Languages across the world can be gendered (language in which the form of noun, verb, or pronoun is presented as female or male) versus genderless. In an ongoing debate, one stream of research suggests that gendered languages are more likely to display gender prejudice than genderless languages. However, another stream of research suggests that language does not have the ability to shape gender prejudice. In this research, we contribute to the debate by using a Natural Language Processing (NLP) method which captures the meaning of a word from the context in which it occurs. Using text data from Wikipedia and the Common Crawl project (which contains text from billions of publicly facing websites) across 45 world languages, covering the majority of the world’s population, we test for gender prejudice in gendered and genderless languages. We find that gender prejudice occurs more in gendered rather than genderless languages. Moreover, we examine whether genderedness of language influences the stereotypic dimensions of warmth and competence utilizing the same NLP method.


2019 ◽  
Vol 25 (06) ◽  
pp. 715-733
Author(s):  
Aline Villavicencio ◽  
Marco Idiart

AbstractIn this paper, we provide an overview of research on multiword expressions (MWEs), from a natural language processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We concentrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods.


Author(s):  
Fazel Keshtkar ◽  
Ledong Shi ◽  
Syed Ahmad Chan Bukhari

Finding our favorite dishes have became a hard task since restaurants are providing more choices and va- rieties. On the other hand, comments and reviews of restaurants are a good place to look for the answer. The purpose of this study is to use computational linguistics and natural language processing to categorise and find semantic relation in various dishes based on reviewers’ comments and menus description. Our goal is to imple- ment a state-of-the-art computational linguistics meth- ods such as, word embedding model, word2vec, topic modeling, PCA, classification algorithm. For visualiza- tions, t-Distributed Stochastic Neighbor Embedding (t- SNE) was used to explore the relation within dishes and their reviews. We also aim to extract the common pat- terns between different dishes among restaurants and reviews comment, and in reverse, explore the dishes with a semantics relations. A dataset of articles related to restaurant and located dishes within articles used to find comment patterns. Then we applied t-SNE visual- izations to identify the root of each feature of the dishes. As a result, to find a dish our model is able to assist users by several words of description and their inter- est. Our dataset contains 1,000 articles from food re- views agency on a variety of dishes from different cul- tures: American, i.e. ’steak’, hamburger; Chinese, i.e. ’stir fry’, ’dumplings’; Japanese, i.e., ’sushi’.


2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Varghese ◽  
S Esteves ◽  
B Kovacic ◽  
A Chatziparasidou ◽  
M Nijs ◽  
...  

Abstract Study question What are the major problems faced by embryologists at 1) Clinic level, 2) Professional level, 3) Personal level, and 4) What are their career goals? Summary answer Embryologists, essential professionals of Fertility Centres, are less satisfied in many quantifiable aspects, but they love their profession and have many aspirational goals. What is known already IVF success depends in part on embryologists’ skills. The need to recognize clinical embryology as a specialty and clinical embryologists’ educational level, responsibilities, and workload have been addressed by a few national societies. However, data are lacking from the embryologists’ viewpoint at a global level about their profession. Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but can be time-consuming, labor-intensive, and prone to bias. Study design, size, duration A questionnaire was prepared using SurveyMonkey online software (SurveyMonkey, Inc., USA) and distributed to IVF lab professionals through embryology societies, online social media, and email databases. The questionnaire consisted of open-ended questions focused on identifying problems faced by embryologists at the clinic, in the profession, and in a personal level, as well as questions about their career outlook. The survey was active from May 2016 until February 2017. From 73 countries, 720 responses were obtained. Participants/materials, setting, methods Using natural language processing (NLP), the top 15 most frequently used keywords were identified and correlated with each other. Stronger correlation (≥0.5) between semantically similar words expressing a strong signal from each answer, and their usage was further analyzed for positive versus negative sentiment. By normalizing the frequency of positive/negative samples for each keyword as a percentage, “sentiment wheels” were produced, identifying the key concepts that respondents answered and quantifying how they felt about them. Main results and the role of chance The responses received were from 80% private, 17% public and 3% other ART settings distributed all over the world. From the embryologists’ viewpoints reported and after the NLP processing it was shown that the common topics related to strong negative sentiments were: embryologists’ remuneration (0.6) at the Clinic level; certification (0.7), recognition (0.5), respect (0.5), learn (0.5) and experience (0.5) at the Professional level; and remuneration (0.7), emotional (0.5) dealing (0.5) at the Personal level. Renumeration was reported and strongly related to embryologists’ viewpoint at both the clinic and personal level in combination with the need for certification, recognition and ongoing development at the Professional level. Moreover, the NLP processing demonstrated that the common topics on career goal analysis related to strong positive sentiments were: teaching (0.7), education (0.7), and continuation (0.5) all three topics are compatible with a professional orientation open to ongoing development and practice advancement. The NLP and the manual data analysis project an image of the typical embryologist as a knowledge seeking professional who is deeply dedicated to the job but feels the need for professional development and suffers some lack of recognition and feels in some cases not fairly treated as an employee. Limitations, reasons for caution The data obtained is limited. Only one natural language processing model was used to analyze the results. Different analysts using other methods may have different results. For these reasons, the results should be interpreted with caution. Wider implications of the findings: It is important to focus on the lab as an organization and not just a service for the patients in treatment at the moment. The NLP results ultimately obtained may help streamline professional satisfaction efforts, and guide future quality management strategies Trial registration number Not applicable


Author(s):  
Natalia Loukachevitch ◽  
Boris Dobrov

AbstractThis chapter describes the Russian RuThes thesaurus created as a linguistic and terminological resource for automatic document processing. Its structure utilizes two popular paradigms for computer thesauri: concept-based units, a small set of relation types, rules for including multiword expression as in information retrieval thesauri; and language-motivated units, detailed sets of synonyms, description of ambiguous words as in WordNet-like thesauri. The development of the RuThes thesaurus is supported for many years: new concepts, new senses, and multiword expressions found in contemporary texts are introduced regularly. The chapter shows some examples of representing newly appeared concepts related to important internal and international events.


1989 ◽  
Vol 33 (19) ◽  
pp. 1334-1338
Author(s):  
Joseph Psotka

Advanced technologies, including artificial intelligence (Al), hypertext, and natural language processing (NLP), are transforming the Mind/Machine Interface. This presentation focuses on two large development projects underway that use these technologies in unique ways. Their use is guided by the three natural means of communication between people: saying, coaching, and showing; as metaphors for using advanced technology interfaces. The two projects are aimed at developing job and training aids for the Army. The most complete example is the Maintenance Aid Computer for HAWK–Intelligent Institutional Instructor (MACH-III). This is the largest and most successful implementation of an ITS to date (Psotka, Massey, and Mutter, 1988). MACH-III was developed by Bolt, Beranek, and Newman (BBN), to provide training in organizational maintenance of the main radar of the HAWK air defense guided missile system. Its core is a huge qualitative simulation of the radar. The complexity of the simulation and the troubleshooting problem space demand a unique hypertext interface, whose structure and function are only beginning to be understood. Some preliminary evaluation results from the U.S. Army Air Defense Artillery School (USAADASCH), Ft. Bliss, Texas are beginning to show its effectiveness. The other project, Building Robust Dual Grammar Exercisers (BRIDGE), will begin to explore the architextual structure of hypertext systems within the context of advanced technologies for military machine translation and military foreign language training. From this perspective, hypertext is a bridging technology that links the existing strengths of qualitative simulations with the future power of natural language processing.


2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.


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