Research Papers On Natural Language Processing

Research Papers On Natural Language Processing-59
The seven papers published in this Research Topic were all reviewed by two independent reviewers. Evaluating Generosity of Article Abstracts in the Environmental Sciences,” Ermakova et al. In fact, the abstract points out the information that is the most important for the reader and is often used as a proxy for the content of an article.The authors propose the GEM score that measures the representativeness of an abstract or its “generosity.” To obtain this score, sections in the papers were weighted according to their importance to the reader and sentences in the abstracts were assigned to different sections based on their similarity with the content of the sections.The study is presented in two companion papers that each provides a different perspective of the analysis.

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The Termolator tool includes chunking that favors chunks containing out-of-vocabulary words, nominalizations, technical adjectives, and other specialized word classes and supports term chunk ranking.

The authors analyse the effectiveness of all involved components to the overall system's performance and compare their Termolator system with a terminology extraction system called Termostat.

and Mariani et al., present the results of an extensive study of a dataset in the field of Natural Language Processing (NLP) and Spoken Language Processing (SLP) for the period 1956–2015.

The authors investigate various trends that can be observed from the publications in this specific research domain.

Their experiments are based on a published dataset of annotated references from a corpus of publications on the historiography of Venice (books and journal articles in Italian, English, French, German, Spanish and Latin) published from the nineteenth century to 2014.

In the evaluation the authors show the relative positive contribution of their character-level word embeddings.

Some of the open source tools for text processing that have been recently applied to such tasks include NLTK, Mallet, Open NLP, Core NLP, Gate, Cite Space, Allen NLP, and others.

Many datasets are now freely available for the community: e.g., Pub Med OA, Cite Seer X, JSTOR, ISTEX, Microsoft Academic Graph, ACL anthology, etc.

In the paper “The Termolator: Terminology Recognition Based on Chunking, Statistical and Search- Based Scores,” Meyers et al.

propose an open-source high-performing terminology extraction system called Termolator which utilizes a combination of knowledge-based and statistical components.


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