Collection of dialogues extracted from subreddits related to Information Technology (IT) and extracted with RDET (Reddit Dataset Extraction Tool). It is composed of 61,842,638 tokens in 179,358 dialogues.
This resource includes the distributional semantic vectors used for the replication of the TakeLab system (https://github.com/nlx-group/arct-rep-rev). The TakeLab system is an automatic classifier for the Argument Reasoning Comprehension Task (https://www.aclweb.org/anthology/S18-1121/). The ...
In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, bec...
Dundee GCG-Bank contains hand-corrected deep syntactic annotations for the Dundee eye-tracking corpus (Kennedy et al., 2003). The annotations are designed to support psycholinguistic investigation into the structural determinants of sentence processing effort. Dundee GCG-Bank is distributed as a ...
Datasets is arff format (for Weka machine learning software) are made available to reproduce the validation experiments presented in the paper.
GREC is a semantically annotated corpus of 240 MEDLINE abstracts (167 on the subject of E. coli species and 73 on the subject of the Human species) which is intended for training IE systems and/or resources which are used to extract events from biomedical literature.
A corpus of 2,019 tweets annotated along each of four emotion dimensions: Valence, Dominance, Arousal and Surprise. Two annotation schemes are used: a 5-point ordinal scale (using SAM manikins for Valence, Arousal and Dominance) and pair-wise comparisons with an "about the same" option (here 2,01...
The HIMERA annotated corpus contains a set of published historical medical documents that have been manually annotated with semantic information that is relevant to the study of medical history and public health. Specifically, annotations correspond to seven different entity types and two differe...
This resource contains model weights for five Transformer-based models: RoBERTa, GPT-2, T5, BART and COMET(BART). These models were implemented using HuggingFace, and fine-tuned on the following four commonsense reasoning tasks: Argument Reasoning Comprehension Task (ARCT), AI2 Reasoning Challen...
Adimen-SUMO is an off-the-shelf first-order ontology that has been obtained by reengineering out of the 88% of SUMO (Suggested Upper Merged Ontology). Adimen-SUMO can be used appropriately by FO theorem provers (like E-Prover or Vampire) for formal reasoning.