Papers by Alessio Palmero Aprosio

6 papers
Adding Gesture, Posture and Facial Displays to the PoliModal Corpus of Political Interviews (2020.lrec-1)

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Challenge: a corpus of face-to-face interviews is annotated with facial displays, hand gestures and body posture.
Approach: They introduce a multimodal corpus on top of transcribed face-to-face interviews that presents the annotation of facial displays, hand gestures and body posture.
Outcome: The proposed corpus is extracted from a larger corpus of 56 face-to-face interviews (14 hours) the annotations include facial displays, hand gestures and body posture.
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement (2021.emnlp-main)

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Challenge: supervised learning is a key component of offensive language detection, but there is little attention given to the quality of annotated data.
Approach: They propose to examine the level of agreement among annotators while selecting data to create offensive language datasets, a task involving a high level of subjectivity.
Outcome: The proposed datasets show that annotators' agreement has a strong effect on classifiers performance and robustness.
There’s Something New about the Italian Parliament: The IPSA Corpus (2024.lrec-main)

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Challenge: despite their potential, the Italian parliamentary documents remain unexplored and inaccessible in their original paper-based form.
Approach: They propose to transform Italian parliamentary documents into a structured corpus . the corpus includes speeches, reports of Standing Committees, and law proposals .
Outcome: The proposed dataset spans 175 years of Italian history spanning from the issuing of the Statuto Albertino in 1848, up to the present day .
Erase and Rewind: Manual Correction of NLP Output through a Web Interface (2021.acl-demo)

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Challenge: In the last years, NLP tools are being used in tasks such as textual inference, machine translation, hate speech detection.
Approach: They propose an NLP annotation software that can be used to manually annotate texts and to fix mistakes in NLP pipelines.
Outcome: The proposed tool can be used to manually annotate texts and fix errors in NLP pipelines, such as Stanford CoreNLP.
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition (2022.lrec-1)

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Challenge: Named-entity recognition is a task that uses named entities to classify texts . annotated data are time and money consuming, since they need to be created by experts of the domain of the annotation that is going to be done .
Approach: They present an Italian dataset for Named-entity recognition with manual annotations and a semi-automatically annotated part.
Outcome: The proposed dataset covers different styles and language uses, and is the largest in Italy.
EasyTurk: A User-Friendly Interface for High-Quality Linguistic Annotation with Amazon Mechanical Turk (2021.eacl-demos)

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Challenge: Amazon Mechanical Turk (AMT) is one of the most popular crowd-sourcing platforms, allowing researchers from all over the world to create linguistic datasets quickly and at a relatively low cost.
Approach: They propose to improve the potential of Amazon Mechanical Turk by adding some new features to the tool.
Outcome: The proposed tool improves the performance of Amazon Mechanical Turk by adding new features.

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