Artificial Intelligence for Science

Our Questions

Artificial Intelligence for Science

🔹How can we help scientists write and communicate their research better? What is the difference between well and poorly written papers?

🔹 What makes an easy-to-read and logically written paper? What are the underlying linguistic patterns of well-written papers?

🔹 How can we apply automatization to aid academic publishers in making the review process more efficient and quicker?

🔹 How can we make the review process of scientific texts more objective? Can papers be evaluated based on quantified factors of writing quality?

Our R+D+i

To solve these investigative questions, we are applying state-of-the-art machine learning techniques, applied linguistic research, and expert knowledge on scientific writing to develop new models, functions, and algorithms.

We seek to comprehensively aid researchers during the entire writing process. This goal will be achieved through our applied research, development, and innovation (R+D+i), merging the latest technological advances with established writing guidelines. Our R+D+i is manifested in WriteWise, a unique software that will modernize scientific writing by reducing the time and effort required by researchers when writing and by journals/academic publishers when reviewing manuscript submissions.

Research Lines

Machine Learning

for Natural Language Processing
Applied to Scientific Writing

We combine machine learning and computational linguistics within the framework of natural language processing, as applied to modelling and revising the writing process and scientific texts. This line of research applies the following methodologies:

1. Novel approaches for representing textual data from scientific articles:

  • Word embeddings combined with deep/machine learning models for natural language processing tasks.
  • Graph-based representations

2. Novel computational approaches for analyzing scientific articles, with specific investigative focus on:

  • Discourse Segmentation
  • Automatic Punctuation Analysis
  • Rule-based Text Mining
  • Topic Modelling
  • Readability/Coherence Classification

and Lexical-Grammar

Applied to Scientific Articles

We use functional and applied discursive frameworks, combined with corpus analysis, computational linguistics, and natural language processing approaches, to empirically determine the discursive and linguistics norms and requirements of academic and scientific texts. This line of research seeks to identify and comprehend the:

1. Communicative purposes and lexical-grammar features that constitute written texts in distinct scientific disciplines.

2. Textual and discursive foundations of academic and scientific texts.


A novel machine learning model that guides graduate students to write more organized and structured texts

Revealing the collaborative dynamics of a large-scale arXiv text collection by means of k-shell decomposition

Sentence encoders as a method for helping users identify and improve semantic similarity in bio-medical text

WriteWise: software that guides scientific writing