Search Results for author:
Found 16 papers, 6 papers with code
PreMOn: a Lemon Extension for Exposing Predicate Models as Linked Data
We introduce PreMOn (predicate model for ontologies), a linguistic resource for exposing predicate models (PropBank, NomBank, VerbNet, and FrameNet) and mappings between them (e. g, SemLink) as Linked Open Data.
Italy goes to Stanford: a collection of CoreNLP modules for Italian
In this we paper present Tint, an easy-to-use set of fast, accurate and extendable Natural Language Processing modules for Italian.
On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning
Systems for automatic extraction of semantic information about events from large textual resources are now available: these tools are capable to generate RDF datasets about text extracted events and this knowledge can be used to reason over the recognized events.
MUSST: A Multilingual Syntactic Simplification Tool
Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications).
Neural Text Simplification in Low-Resource Conditions Using Weak Supervision
Inspired by the machine translation field, in which synthetic parallel pairs generated from monolingual data yield significant improvements to neural models, in this paper we exploit large amounts of heterogeneous data to automatically select simple sentences, which are then used to create synthetic simplification pairs.
Adding Gesture, Posture and Facial Displays to the PoliModal Corpus of Political Interviews
This paper introduces a multimodal corpus in the political domain, which on top of transcribed face-to-face interviews presents the annotation of facial displays, hand gestures and body posture.
Creating a Multimodal Dataset of Images and Text to Study Abusive Language
We find that users judge the same images in different ways, although the presence of a person in the picture increases the probability to get an offensive comment.
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language Detection
In this paper we present our submission to sub-task A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2).
Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection
We first re-annotate part of a widely used dataset for abusive language detection in English in two conditions, i. e. with and without context.
EasyTurk: A User-Friendly Interface for High-Quality Linguistic Annotation with Amazon Mechanical Turk
Amazon Mechanical Turk (AMT) has recently become 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.
Erase and Rewind: Manual Correction of NLP Output through a Web Interface
In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP.
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition
The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically annotated part.
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
BERToldo, the Historical BERT for Italian
This has led to the creation of BERT-like models for different languages trained with digital repositories from the past.
