Pinned Repositories
CROMER
CROMER (CROss-document Main Events and entities Recognition), is a tool for cross-document coreference
E3C-Corpus
E3C is a freely available multilingual corpus (Italian, English, French, Spanish, and Basque) of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, and training of information extraction systems. It consists of two types of annotations: (i) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (i.e. SNOMED-CT, ICD-10); and (ii) temporal information and factuality: events, time expressions, and temporal relations according to the THYME standard. The corpus is organised into three layers, with different purposes. Layer 1: about 25K tokens per language with full manual annotation of clinical entities, temporal information and factuality, for benchmarkingand linguistic analysis. Layer 2: 50-100K tokens per language with semi-automatic annotations of clinical entities, to be used to train baseline systems. Layer 3: about 1M tokens per language of non-annotated medical documents to be exploited by semi-supervised approaches. Researchers can use the benchmark training and test splits of our corpus to develop and test their own models. We trained several deep learning based models and provide baselines using the benchmark. Both the corpus and the built models will be available through the ELG platform.
EOP-1.1.2
Excitement Open Platform for Textual Entailment - Release 1.1.2
EOP-1.1.3
Excitement Open Platform for Textual Entailment - Release 1.1.3
EOP-1.1.4
Excitement Open Platform for Textual Entailment - Release 1.1.4
EOP-1.2.1
Excitement Open Platform for Textual Entailment - Release 1.2.1
EOP-1.2.3
Excitement Open Platform for Textual Entailment - Release 1.2.3
Excitement-Open-Platform
Excitement Open Platform for Recognizing Textual Entailments
Excitement-TDMLEDA
MT-EQuAl
a Toolkit for Manual Assessment of Machine Translation Output
NLP / FBK's Repositories
hltfbk/Excitement-Open-Platform
Excitement Open Platform for Recognizing Textual Entailments
hltfbk/E3C-Corpus
E3C is a freely available multilingual corpus (Italian, English, French, Spanish, and Basque) of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, and training of information extraction systems. It consists of two types of annotations: (i) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (i.e. SNOMED-CT, ICD-10); and (ii) temporal information and factuality: events, time expressions, and temporal relations according to the THYME standard. The corpus is organised into three layers, with different purposes. Layer 1: about 25K tokens per language with full manual annotation of clinical entities, temporal information and factuality, for benchmarkingand linguistic analysis. Layer 2: 50-100K tokens per language with semi-automatic annotations of clinical entities, to be used to train baseline systems. Layer 3: about 1M tokens per language of non-annotated medical documents to be exploited by semi-supervised approaches. Researchers can use the benchmark training and test splits of our corpus to develop and test their own models. We trained several deep learning based models and provide baselines using the benchmark. Both the corpus and the built models will be available through the ELG platform.
hltfbk/EOP-1.2.1
Excitement Open Platform for Textual Entailment - Release 1.2.1
hltfbk/CROMER
CROMER (CROss-document Main Events and entities Recognition), is a tool for cross-document coreference
hltfbk/MT-EQuAl
a Toolkit for Manual Assessment of Machine Translation Output
hltfbk/EOP-1.2.3
Excitement Open Platform for Textual Entailment - Release 1.2.3
hltfbk/EOP-1.1.2
Excitement Open Platform for Textual Entailment - Release 1.1.2
hltfbk/EOP-1.1.3
Excitement Open Platform for Textual Entailment - Release 1.1.3
hltfbk/EOP-1.1.4
Excitement Open Platform for Textual Entailment - Release 1.1.4
hltfbk/Excitement-TDMLEDA
hltfbk/Excitement-Transduction-Layer
hltfbk/cas_access_example
This small code shows how you can annotate, access, serialize CAS with UIMA (J)CAS APIs.
hltfbk/EOP-1.0.2
Excitement Open Platform for Textual Entailment - Release 1.0.2
hltfbk/EOP-1.1.1
Excitement Open Platform for Textual Entailment - Release 1.1.1
hltfbk/EOP-1.2.0
Excitement Open Platform for Textual Entailment - Release 1.2.0
hltfbk/lm-evaluation-harness
A framework for few-shot evaluation of language models.