This is a demonstration of our NLP system: HMTL is a neural model for resolving four fundamental tasks in NLP, namely Named Entity Recognition, Entity Mention Detection, Relation Extraction and Coreference Resolution using multi-task learning.
For a brief introduction to multi-task learning, you can refer to our blog post. Each of the four tasks considered is detailed in the following section.
The web interface for the demo can be found here for you to try and play with it. HMTL comes with the web visualization client if you prefer to run on your local machine. The demo (and the released weights) are for English.
Description of the tasks
Named Entity Recognition (NER)
Named Entity Recognition aims at identifying and clasifying named entities (real-world object, such as persons, locations, etc. that can be denoted with a proper name).
[Homer Simpson]PERS lives in [Springfield]LOC with his wife and kids.
HMTL is trained on OntoNotes 5.0 and can recognized various types (18) of named entities: PERSON, NORP, FAC, ORG, GPE, LOC, etc.
Entity Mention Detection (EMD)
Entity Mention Detection aims at identifying and clasifying entity mentions (real-world object, such as persons, locations, etc. that are not necessarily denoted with a proper name).
[The men]PERS held on [the sinking vessel]VEH until [the ship]VEH was able to reach them from [Corsica]LOC.
HMTL can recognized different types of mentions: PER, GPE, ORG, FAC, LOC, WEA and VEH.
Relation Extraction (RE)
Relation extraction aims at extracting the semantic relations between the mentions.
Coreference Resolution (CR)
In a text, two or more expressions can link to the same person or thing in the worl. Coreference Resolution aims at finding the coreferent spans and cluster them.