56,666 research outputs found
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
A reproducible approach with R markdown to automatic classification of medical certificates in French
In this paper, we report the ongoing developments of our first participation to the Cross-Language Evaluation Forum (CLEF) eHealth Task 1: âMultilingual Information Extraction - ICD10 codingâ (NĂ©vĂ©ol et al., 2017). The task consists in labelling death certificates, in French with international standard codes. In particular, we wanted to accomplish the goal of the âReplication trackâ of this Task which promotes the sharing of tools and the dissemination of solid, reproducible results.In questo articolo presentiamo gli sviluppi del lavoro iniziato con la partecipazione al Laboratorio CrossLanguage Evaluation Forum (CLEF) eHealth denominato: âMultilingual Information Extraction - ICD10 codingâ (NĂ©vĂ©ol et al., 2017) che ha come obiettivo quello di classificare certificati di morte in lingua francese con dei codici standard internazionali. In particolare, abbiamo come obiettivo quello proposto dalla âReplication trackâ di questo Task, che promuove la condivisione di strumenti e la diffusione di risultati riproducibili
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