Unsupervised relation extraction ( URE ) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases ( KBs ) . URE methods can be categorised into generative and discriminative approaches , which rely either on hand-crafted features or surface form . However , we demonstrate that by using only named entities to induce relation types , we can outperform existing methods on two popular datasets . We conduct a comparison and evaluation of our findings with other URE techniques , to ascertain the important features in URE . We conclude that entity types provide a strong inductive bias for URE . 1 Relation extraction ( RE ) extracts semantic relations between entities from plain text . For instance , " Jon Robin Baitz head , born in Los Angeles tail ... " expresses the relation /people / person / place of birth between the two head-tail entities . Extracted relations are then used for several downstream tasks such as information retrieval ( Corcoglioniti et al . , 2016 ) and knowledge base construction ( Al-Zaidy and Giles , 2018 ) . RE has been widely studied using fully supervised learning ( Nguyen and Grishman , 2015 ; Miwa and Bansal , 2016 ; Zhang et al . , 2017 Zhang et al . , , 2018 ) ) and distantly supervised approaches ( Mintz et al . , 2009 ; Riedel et al . , 2010 ; Lin et al . , 2016 ) . Unsupervised relation extraction ( URE ) methods have not been explored as much as fully or distantly supervised learning techniques . URE is promising , since it does not require manually annotated data nor human curated knowledge bases ( KBs ) , which are expensive to produce . Therefore , it can be applied to domains and languages where annotated data and KBs are not available . Moreover , URE can discover new relation types , since it is not restricted to specific relation types in the same way as fully and distantly supervised methods . One might argue that Open Information Extraction ( OpenIE ) can also discover new relations . However , OpenIE identifies relations based on textual surface information . Thus , similar relations with different textual forms may not be recognised . Unlike OpenIE , URE groups similar relations into clusters . Despite these advantages , there are only a few attempts tackling URE using machine learning ( ML ) ( Hasegawa et al . , 2004 ; Banko et al . , 2007 ; Yao et al . , 2011 ; Marcheggiani and Titov , 2016 ; Simon et al . , 2019 ) . Similarly to other unsupervised learning tasks , a challenge in URE is how to evaluate results . Recent approaches ( Yao et al . , 2011 ; Marcheggiani and Titov , 2016 ; Simon et al . , 2019 ) employ a widely used data generation setting in distantly supervised RE , i.e. , aligning a large amount of raw text against triplets in a curated KB . A standard metric score is computed by comparing the output relation clusters against the automatically annotated relations . In particular , the NYT-FB dataset ( Marcheggiani and Titov , 2016 ) which is used for evaluation , has been created by mapping relation triplets in Freebase ( Bollacker et al . , 2008 ) against plain text articles in the New York Times ( NYT ) corpus ( Sandhaus , 2008 ) . Standard clustering evaluation metrics for URE include B 3 ( Bagga and Baldwin , 1998 ) , V-measure ( Rosenberg and Hirschberg , 2007 ) , and ARI ( Hubert and Arabie , 1985 ) . Although the above mentioned experimental setting can be created automatically , there are three challenges to overcome . Firstly , the development and test sets are silver , i.e. , they include noisy labelled instances , since they are not human-curated . Secondly , the development and test sentences are part of the training set , i.e. , a transductive setting . It is thus unclear how well the existing models perform on unseen sentences . Finally , NYT-FB can be considered highly imbalanced , since only 2.1 % of the training sentences can be aligned with Freebase 's triplets . Due to the noisy nature of silver data ( NYT-FB ) , evaluation on silver data will not accurately reflect the system performance . We also need unseen data during testing to examine the system generalisation . To overcome these challenges , we will employ the test set of TACRED ( Zhang et al . , 2017 ) , a widely used manually annotated corpus . Regarding the imbalanced data , we will demonstrate that in fact around 60 % ( instead of 2.1 % ) of instances in the training set express relation types defined in Freebase . In this work , we present a simple URE approach relying only on entity types that can obtain improved performance compared to current methods . Specifically , given a sentence consisting of two entities and their corresponding entity types , e.g. , PERSON and LOCATION , we induce relations as the combination of entity types , e.g. , PERSON-LOCATION . It should be noted that we employ only entity types because their combinations form reasonably coarse relation types ( e.g. , PERSON-LOCATION covers /people / person / place of birth defined in Freebase ) . We further discuss our improved performance in § 3 . Our contributions are as follows : ( i ) We perform experiments on both automatically / manuallylabelled datasets , namely NYT-FB and TACRED , respectively . We show that two methods using only entity types can outperform the state-of-theart models including both feature-engineering and deep learning approaches . The surprising results raise questions about the current state of unsupervised relation extraction . ( ii ) For model design , we show that link predictor provides a good signal to train a URE model ( Fig 1 ) . We also illustrate that entity types are a strong inductive bias for URE ( Table 1 ) . We have shown the importance of entity types in URE . Our methods use only entity types , yet they yield higher performance than previous work on both NYT-FB and TACRED . We have investigated the current experimental setting , concluding that a strong inductive bias is required to train a relation extraction model without labelled data . URE remains challenging , which requires improved methods to deal with silver data . We also plan to use different types of labelled data , e.g. , domain specific data sets , to ascertain whether entity type information is more discriminative in sub-languages .

Challenge: Unsupervised relation extraction has not been explored although it can be applied to languages and domains where annotated data or knowledge bases are not available.
Approach: They propose an approach which only relies on entity types and performs a comparison with other unsupervised approaches to find important features.
Outcome: They find that only using entity types can outperform existing methods on two datasets indicating that they provide a strong inductive bias.