Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
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We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Ranked #1 on Natural Language Inference on MultiNLI (Accuracy metric)
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications.
The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.
Ranked #9 on Relation Extraction on ACE 2005 (using extra training data)
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
Ranked #1 on Entity Typing on Open Entity
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #1 on Named Entity Recognition on NCBI-disease
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
Ranked #1 on Sentence Classification on ScienceCite (using extra training data)
CITATION INTENT CLASSIFICATION DEPENDENCY PARSING LANGUAGE MODELLING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks.
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #1 on Text Classification on 20NEWS (using extra training data)