Élaboration d’outils méthodologiques pour décrire les prédicats du français

2007 ◽  
Vol 30 (2) ◽  
pp. 217-245 ◽  
Author(s):  
Aude Grezka ◽  
Pierre-André Buvet
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Point Of View ◽  
Theoretical Point ◽  
Satisfactory Description

A satisfactory description of the predicates from the theoretical point of view is necessary when elaborating electronic dictionaries meant for natural language processing. We focus on the methodology and descriptors we use within the framework of the classes of objects. At the same time, we put forward complementary descriptors so as to deal with predicative polysemy. Our analysis is illustrated by means of perception predicates.

Natural Language Processing

1996 ◽  
Vol 16 ◽  
pp. 70-85 ◽  
Author(s):  
Thomas C. Rindflesch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Domain Knowledge ◽  
The State ◽  
Point Of View ◽  
Computer Applications ◽  
Significant Progress ◽  
Future Directions

Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

scholarly journals Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks

2020 ◽  
Vol 26 (1) ◽  
pp. 375-384
Author(s):  
Alexey Kolesnikov ◽  
Pavel Kikin ◽  
Giovanni Niko ◽  
Elena Komissarova
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Extraction ◽  
Named Entity Recognition ◽  
Point Of View ◽  
Entity Recognition ◽  
Named Entities ◽  
Processing Technologies ◽  
Plain Text

Modern natural language processing technologies allow you to work with texts without being a specialist in linguistics. The use of popular data processing platforms for the development and use of linguistic models provides an opportunity to implement them in popular geographic information systems. This feature allows you to significantly expand the functionality and improve the accuracy of standard geocoding functions. The article provides a comparison of the most popular methods and software implemented on their basis, using the example of solving the problem of extracting geographical names from plain text. This option is an extended version of the geocoding operation, since the result also includes the coordinates of the point features of interest, but there is no need to separately extract the addresses or geographical names of the objects in advance from the text. In computer linguistics, this problem is solved by the methods of extracting named entities (Eng. named entity recognition). Among the most modern approaches to the final implementation, the authors of the article have chosen algorithms based on rules, models of maximum entropy and convolutional neural networks. The selected algorithms and methods were evaluated not only from the point of view of the accuracy of searching for geographical objects in the text, but also from the point of view of simplicity of refinement of the basic rules or mathematical models using their own text bodies. Reports on technological violations, accidents and incidents at the facilities of the heat and power complex of the Ministry of Energy of the Russian Federation were selected as the initial data for testing the abovementioned methods and software solutions. Also, a study is presented on a method for improving the quality of recognition of named entities based on additional training of a neural network model using a specialized text corpus.

Produktivität und Idiomatizität von Präposition-Substantiv-Sequenzen

2007 ◽  
Vol 26 (2) ◽  
Author(s):  
Tibor Kiss
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Point Of View ◽  
Fundamental Properties ◽  
Combining Evidence ◽  
Statistical Natural Language Processing ◽  
German Newspaper ◽  
Empirical Point

AbstractThe present paper discusses two fundamental properties of so-called determinerless PPs, their idiomaticity and productivity. Combining evidence from German newspaper corpora with methods from statistical natural language processing, we can show that determinerless PPs are no more idiomatic that ordinary phrasal combinations in general. From an empirical point of view determinerless PPs can be classified as productive. Consequently determinerless PPs should not be treated as idiomatic exceptions, which can be covered by finite lists.

scholarly journals An Overview of the Basic NLP Resources Towards Building the Assamese-English Machine Translation

2021 ◽  
Author(s):  
Nibedita Roy ◽  
Apurbalal Senapati
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Daily Life ◽  
Point Of View ◽  
Advanced Stage ◽  
Computational Point ◽  
Input Text ◽  
Level Performance

Machine Translation (MT) is the process of automatically converting one natural language into another, preserving the exact meaning of the input text to the output text. It is one of the classical problems in the Natural Language Processing (NLP) domain and there is a wide application in our daily life. Though the research in MT in English and some other language is relatively in an advanced stage, but for most of the languages, it is far from the human-level performance in the translation task. From the computational point of view, for MT a lot of preprocessing and basic NLP tools and resources are needed. This study gives an overview of the available basic NLP resources in the context of Assamese-English machine translation.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

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.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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