Until very recently, most NLP tasks (e.g., parsing, tag-ging, etc.) have been confined to a very limited number of languages, the so-called majority languages. Now, as the field moves into the era of developing tools fo
Corpus data have emerged as the raw data/benchmark for several NLP applications. Corpus is described...
The key argument of this dissertation is that the success of an Natural Language Processing (NLP) ap...
Natural Language Processing (NLP) is a 'theoretically motivated range of computational techniques fo...
The field of natural language processing (aka NLP) is an intersection of the study of linguistics, c...
Research in Natural Language Processing (NLP) has in recent years benefited from the enormous amount...
This paper discusses the role of low-resource languages in NLP through the lens of different stakeho...
Linguistic typology aims to capture structural and semantic variation across the world’s languages. ...
Annotated corpora are sets of structured text used to enable Natural Language Pro-cessing (NLP) task...
The role of natural language is becoming in these years a more and more acknowledged aspect of the S...
Abstract. The role of natural language is becoming in these years a more and more acknowledged aspec...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
This paper explores the potential for an-notating and enriching data for low-density languages via t...
Natural language processing (NLP) is a rapidly growing field with a wide range of applications, such...
Real world data differs radically from the benchmark corpora we use in natural language processing (...
The paper starts with the history of Natural Language Processing (NLP) and revisits the concepts and...
Corpus data have emerged as the raw data/benchmark for several NLP applications. Corpus is described...
The key argument of this dissertation is that the success of an Natural Language Processing (NLP) ap...
Natural Language Processing (NLP) is a 'theoretically motivated range of computational techniques fo...
The field of natural language processing (aka NLP) is an intersection of the study of linguistics, c...
Research in Natural Language Processing (NLP) has in recent years benefited from the enormous amount...
This paper discusses the role of low-resource languages in NLP through the lens of different stakeho...
Linguistic typology aims to capture structural and semantic variation across the world’s languages. ...
Annotated corpora are sets of structured text used to enable Natural Language Pro-cessing (NLP) task...
The role of natural language is becoming in these years a more and more acknowledged aspect of the S...
Abstract. The role of natural language is becoming in these years a more and more acknowledged aspec...
NLP technologies are uneven for the world's languages as the state-of-the-art models are only availa...
This paper explores the potential for an-notating and enriching data for low-density languages via t...
Natural language processing (NLP) is a rapidly growing field with a wide range of applications, such...
Real world data differs radically from the benchmark corpora we use in natural language processing (...
The paper starts with the history of Natural Language Processing (NLP) and revisits the concepts and...
Corpus data have emerged as the raw data/benchmark for several NLP applications. Corpus is described...
The key argument of this dissertation is that the success of an Natural Language Processing (NLP) ap...
Natural Language Processing (NLP) is a 'theoretically motivated range of computational techniques fo...