Relationship extraction is the task of extracting semantic relationships from a text. Extracted relationships usually occur between two or more entities of a certain type (e.g. Person, Organisation, Location) and fall into a number of semantic categories (e.g. married to, employed by, lives in).
New York Times Corpus
The standard corpus for distantly supervised relationship extraction is the New York Times (NYT) corpus, published in Riedel et al, 2010.
This contains text from the New York Times Annotated Corpus with named entities extracted from the text using the Stanford NER system and automatically linked to entities in the Freebase knowledge base. Pairs of named entities are labelled with relationship types by aligning them against facts in the Freebase knowledge base. (The process of using a separate database to provide label is known as ‘distant supervision’)
Elevation Partners, the $1.9 billion private equity group that was founded by Roger McNamee
(founded_by, Elevation_Partners, Roger_McNamee)
Different papers have reported various metrics since the release of the dataset, making it difficult to compare systems directly. The main metrics used are either precision at N results or plots of the precision-recall. The range of recall has increased over the years as systems improve, with earlier systems having very low precision at 30% recall.
|Model||P@10%||P@30%||Paper / Source||Code|
|RESIDE (Vashishth et al., 2018)||73.6||59.5||RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information||RESIDE|
|PCNN+ATT (Lin et al., 2016)||69*||51*||Neural Relation Extraction with Selective Attention over Instances||OpenNRE|
|MIML-RE (Surdeneau et al., 2012)||61*+||-||Multi-instance Multi-label Learning for Relation Extraction||Mimlre|
|MultiR (Hoffman et al., 2011)||60*+||-||Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations||MultiR|
|(Mintz et al., 2009)||40*+||-||Distant supervision for relation extraction without labeled data|
(*) Estimated from plots using WebplotDigitizer. These are reported to two significant digits due to the low accuracy when extracting from graphs.
(+) Estimated from results in the paper “Neural Relation Extraction with Selective Attention over Instances”
SemEval-2010 Task 8
SemEval-2010 introduced ‘Task 8 - Multi-Way Classification of Semantic Relations Between Pairs of Nominals’. The task is, given a sentence and two tagged nominals, to predict the relation between those nominals and the direction of the relation. The dataset contains nine general semantic relations together with a tenth ‘OTHER’ relation.
There were apples, pears and oranges in the bowl.
(content-container, pears, bowl)
The main evaluation metric used is macro-averaged F1, averaged across the nine proper relationships (i.e. excluding the OTHER relation), taking directionality of the relation into account.
Several papers have used additional data (e.g. pre-trained word embeddings, WordNet) to improve performance. The figures reported here are the highest achieved by the model using any external resources.
*: It uses external lexical resources, such as WordNet, part-of-speech tags, dependency tags, and named entity tags.
|Model||F1||Paper / Source||Code|
|BRCNN (Cai et al., 2016)||86.3||Bidirectional Recurrent Convolutional Neural Network for Relation Classification|
|DRNNs (Xu et al., 2016)||86.1||Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation|
|depLCNN + NS (Xu et al., 2015a)||85.6||Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling|
|SDP-LSTM (Xu et al., 2015b)||83.7||Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path||Sshanu’s Reimplementation|
|DepNN (Liu et al., 2015)||83.6||A Dependency-Based Neural Network for Relation Classification|
|FCN (Yu et al., 2014)||83.0||Factor-based compositional embedding models|
|MVRNN (Socher et al., 2012)||82.4||Semantic Compositionality through Recursive Matrix-Vector Spaces||pratapbhanu’s Reimplementation|