Natural Language Processing: Challenges and Future Directions

2020 ◽  
pp. 236-246
Author(s):  
Zakaria Kaddari ◽  
Youssef Mellah ◽  
Jamal Berrich ◽  
Mohammed G. Belkasmi ◽  
Toumi Bouchentouf
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Future Directions

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 Towards a General-Purpose Linguistic Annotation Backend

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Graham Neubig ◽  
Patrick Littell ◽  
Chian-Yu Chen ◽  
Jean Lee ◽  
Zirui Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
General Purpose ◽  
Language Documentation ◽  
Training Material ◽  
Future Directions ◽  
Recent Advances ◽  
Linguistic Annotation

Language documentation is inherently a time-intensive process; transcription, glossing, and corpus management consume a significant portion of documentary linguists’ work. Advances in natural language processing can help to accelerate this work, using the linguists’ past decisions as training material, but questions remain about how to prioritize human involvement. In this extended abstract, we describe the beginnings of a new project that will attempt to ease this language documentation process through the use of natural language processing (NLP) technology. It is based on (1) methods to adapt NLP tools to new languages, based on recent advances in massively multilingual neural networks, and (2) backend APIs and interfaces that allow linguists to upload their data (§2). We then describe our current progress on two fronts: automatic phoneme transcription, and glossing (§3). Finally, we briefly describe our future directions (§4).

scholarly journals Recent advances in processing negation

2020 ◽  
pp. 1-10
Author(s):  
Roser Morante ◽  
Eduardo Blanco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Future Directions ◽  
Rule Based ◽  
Computational Approaches ◽  
Recent Advances ◽  
Linguistic Phenomenon

Abstract Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

Dagstuhl seminar 19461 on conversational search

2020 ◽  
Vol 54 (1) ◽  
pp. 1-11
Author(s):  
Avishek Anand ◽  
Lawrence Cavedon ◽  
Matthias Hagen ◽  
Hideo Joho ◽  
Mark Sanderson ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Human Computer Interaction ◽  
Language Processing ◽  
Research Agenda ◽  
Web Search ◽  
Dialogue Systems ◽  
Future Directions ◽  
Working Groups

In the week of November 10--15, 2019, 44 researchers from the fields of information retrieval and Web search, natural language processing, human computer interaction, and dialogue systems met for the Dagstuhl Seminar 19461 "Conversational Search" to share the latest development in the area of conversational search and discuss its research agenda and future directions. The clear signal from the seminar is that research opportunities to advance conversational search are available to many areas and that collaboration in an interdisciplinary community is essential to achieve the goals. This report overviews the program and selected findings of the working groups.

scholarly journals Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (Extended Abstract)

2017 ◽  
Author(s):  
Raffaella Bernardi ◽  
Ruket Cakici ◽  
Desmond Elliott ◽  
Aykut Erdem ◽  
Erkut Erdem ◽  
...  
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Challenging Problem ◽  
Image Description ◽  
Future Directions ◽  
Evaluation Measures ◽  
Advantages And Disadvantages

Automatic image description generation is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the known approaches based on how they conceptualise this problem and provide a review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image-text datasets and the evaluation measures that have been developed to assess the quality of machine-generated descriptions. Finally we explore future directions in the area of automatic image description.

Natural language processing for smart construction: Current status and future directions

2022 ◽  
Vol 134 ◽  
pp. 104059
Author(s):  
Chengke Wu ◽  
Xiao Li ◽  
Yuanjun Guo ◽  
Jun Wang ◽  
Zengle Ren ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Current Status ◽  
Future Directions
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