Search Results for author: Carlos-Emiliano González-Gallardo

Found 11 papers, 2 papers with code

Efficient Social Network Multilingual Classification using Character, POS n-grams and Dynamic Normalization

no code implementations21 Feb 2017 Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno, Azucena Montes Rendón, Gerardo Sierra

In this paper we describe a dynamic normalization process applied to social network multilingual documents (Facebook and Twitter) to improve the performance of the Author profiling task for short texts.

General Classification POS

WiSeBE: Window-based Sentence Boundary Evaluation

1 code implementation27 Aug 2018 Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno

Do standard evaluation metrics like precision, recall, F-score or classification error; and more important, evaluating an automatic system against a unique reference is enough to conclude how well a SBD system is performing given the final application of the transcript?

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)

no code implementations14 Apr 2020 Carlos-Emiliano González-Gallardo, Eric SanJuan, Juan-Manuel Torres-Moreno

In this paper we define the concept of Interestingness as a generalization of Informativeness, whereby the information need is diverse and formalized as an unknown set of implicit queries.

Information Retrieval Informativeness +4

Yes but.. Can ChatGPT Identify Entities in Historical Documents?

1 code implementation30 Mar 2023 Carlos-Emiliano González-Gallardo, Emanuela Boros, Nancy Girdhar, Ahmed Hamdi, Jose G. Moreno, Antoine Doucet

Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents.

named-entity-recognition Named Entity Recognition +1

A Comprehensive Survey of Document-level Relation Extraction (2016-2023)

no code implementations28 Sep 2023 Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano González-Gallardo, Georgeta Bordea, Nicolas Sidere, Antoine Doucet

Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) concerned with identifying and extracting relationships between entities beyond sentence boundaries.

Document-level Relation Extraction Relation +1

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