XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference

1 . Introduction . Social media , including online discussion forums and commenting systems , provide convenient platforms for the public to voice opinions and discuss trending events [ O ’ Connor et al. , 2010 , Lau et al. , 2012 ] . Entity linking ( EL ) , which aims to identify the knowledge base entry ( or the lack thereof ) for a given mention ’ s span , has become an indispensable tool for consuming the enormous amount of social media posts . Concretely , automatically recognizing entities can quickly inform who and what are popular [ O ’ Connor et al. , 2010 , Zhao et al. , 2014 , Dredze et al. , 2016 ] , promote semantic understanding of social media content , and facilitate downstream tasks , such as relation extraction , opinion mining , questions answering , and personalized recommendation [ Messenger and Whittle , 2011 , Galli et al. , 2015 ] . Although EL has been extensively investigated in newswire [ Kazama and Torisawa , 2007 , Ratinov et al. , 2011 ] , web pages [ Demartini et al. , 2012 ] , and broadcast news [ Benton and Dredze , 2015 ] , its study in the social media domain was only started more recently [ Guo et al. , 2013a , Yang and Chang , 2015 , Moon et al. , 2018 ] , mostly focusing on English . In this paper , we study the task of entity linking for user comments in online Chinese news portals . To the best of our knowledge , we are the first to investigate EL problem for the genre of news comments at a large scale . Besides issues present in the conventional EL work [ Ji et al. , 2010 ] , social media text poses additional challenges : the lack of context and increased name variations due to its informal style . State-of-the-art EL methods [ FrancisLandau et al. , 2016 , Gupta et al. , 2017 ] heavily rely on modeling the text surrounding the mentions , as the abundant context from longer documents greatly helps identify entity related content . However , context is often scant for user comments . For instance , as shown in Figure 1 , the entity mention m2 may indicate “ Bingbing Fan ” or “ Bingbing Li ” , both being prominent actresses . By looking up the entities covered in the article , which contains unambiguous mention of the former entity , an EL system will be more confident to link m2 to it . Moreover , the informal style and evolving vocabulary on social media lead to enormous name variations based on aliases , morphing , and misspelling . For instance , in one of our newly annotated datasets , the maximum number of distinct mentions of an entity is 121 . In this work , we propose XREF , a novel entity linking model for Chinese news comments by exploiting context information of entity mentions as well as identifying relevant entities in reference articles . XREF , with its overview displayed in Figure 2 , has three key properties . First , we enrich the mention representation with two sources of information through attentions . Comment attention pinpoints topics involving the target entity from comment context . For instance , words “ star ” and “ actress ” in comment C1 in Figure 1 provide useful information about entity types . Article entity attention detects target entities from the articles if they are discussed . Furthermore , we investigate a new objective function to drive the learning of article entity attention . Finally , we also exploit data augmentation with distant supervision [ Mintz et al. , 2009 ] to leverage large amounts of unlabeled comments and articles for model training . Since there was no publicly-available annotated dataset , as part of this study , we collect and label two new datasets of Chinese news comments from the domain of entertainment and product , which are crawled from a popular Chinese news portal toutiao.com.1 Experimental results show that our best performing model obtains significantly better accuracy 1 . Datasets and code can be found at http : //xinyuhua.github.io/Resources/akbc20/ . Xref : Entity Linking for Chinese News Comments m ) and article ( vartm ) via attention mechanisms . Entity representation is built on entity-entity and entityword co-occurrence graph embeddings . The dot product of the mention representation and entity representation can be concatenated with a feature vector to produce the final output after a layer of linear transformation . and Mean Reciprocal Rank scores than the state-of-the-art [ Le and Titov , 2018 ] and other competitive comparisons . For example , our model improves the accuracy by at least three points over the state-of-the-art model in both domains with NIL mentions considered ( 67.2 vs 58.6 on entertainment comments , and 77.3 vs 68.6 on product comments ) . 2 . Related Work . Entity linking ( EL ) , as a fundamental task for information extraction , has been extensively studied for long documents , such as news articles or web pages [ Ji et al. , 2010 , Shen et al. , 2015 ] . State-of-the-art EL systems rely on extensive resources for learning to represent entities with diverse information , including entity descriptions given by Wikipedia or knowledge bases [ Kazama and Torisawa , 2007 , Cucerzan , 2007 ] , entity types and relations with other entities [ Bunescu and Paşca , 2006 , Hoffart et al. , 2011b , Kataria et al. , 2011 ] , and the surrounding context [ Ratinov et al. , 2011 , Sun et al. , 2015 ] . Neural network-based models are designed to learn a similarity measure between a given entity mention and previously acquired entity representation [ Francis-Landau et al. , 2016 , Gupta et al. , 2017 , Le and Titov , 2018 ] . However , very limited context is provided in social media posts . In this work , we propose to leverage attention mechanisms to identify salient content from both comments and the corresponding articles to enrich the entity mention representation . Our work is inline with the emerging entity linking research for social media content [ Liu et al. , 2013 , Guo et al. , 2013a , b , Fang and Chang , 2014 , Hua et al. , 2015 , Yang and Chang , 2015 ] . To overcome the lack of context , existing models mostly resort to including extra information , e.g. , considering historical messages by the same authors or socially-connected authors [ Guo et al. , 2013b , Shen et al. , 2013 , Yang et al. , 2016 ] , or leveraging posts of similar content [ Huang et al. , 2014 ] . However , users in news commenting systems might be anonymous , and few additional posts would be available for newly published articles . We therefore study a more practical setup , without using any of the aforementioned information as input . 3 . Data Collection and Annotation . We collect user comments along with corresponding news articles from toutiao.com , a popular Chinese online news portal . A sample article snippet with comments is displayed in Figure 1 . Two popular domains are selected for annotation : entertainment ( Ent ) and product ( Prod ) . Articles and comments in Ent focus on movies , TV shows , and celebrities , whereas most topics in Prod are automobiles and electronic products . The statistics of the crawled dataset after filtering are in Table 1 . As illustrated , there are only an average of 20 characters in a comment , highlighting the lack of context . Annotation Procedure . We randomly sample 995 articles from Ent and 783 articles from Prod , and annotate the corresponding user comments . Articles and comments that are not in the samples are used for model pre-training via data augmentation ( § 4.6 ) . Annotators are presented with both comments and corresponding articles during the annotation process . They first identify mention spans , where named , nominal , and pronominal mentions of entities are labeled . Each mention is then linked to an entity in a knowledge base , or labeled as NIL if no entry is found . Though not in our knowledge base , the word “ 小编 ” ( editor ) is included as an entity due to its popularity . We also allow one mention to be linked to multiple entities , e.g . plural pronoun “ 他们 ” ( they/them ) . Comments without any mention are discarded . 13 professional annotators , who are native Chinese speakers with extensive NLP annotation experience , are hired , each annotating a different subset . An additional human annotator conducts the final check . Statistics . Final statistics for the datasets are displayed in Table 1 . On average , there are 4.4 distinct mentions per entity , with a maximum number of 121 for domain Ent . For Prod , the average mention number is 2.9 with a maximum number of 48 . Sample mentions are shown in Table 2 . We categorize the samples into the following types , based on the entities mentioned by : ( 1 ) canonical names as defined in knowledge base ; ( 2 ) nicknames as the popular aliases included in the knowledge base for each entity ; ( 3 ) pronominal mentions indicating one Xref : Entity Linking for Chinese News Comments entity , such as “ 他 ( he/him ) ” or “ 这个 ( this ) ” ; ( 4 ) plural pronominal mentions that are linked to multiple entities ; ( 5 ) others , all other types of mentions that can be linked to the KB , including aliases not in the knowledge base or misspellings ; and ( 6 ) NIL , mentions that can not be linked to any entity in the KB . The mention type distributions are in Table 3 , and it is observed that pronominal and nickname mentions are more common in Ent where celebrities are frequently discussed . The type of others is more significant in Prod due to the prevalent usage of irregular name variations for products . Knowledge Base . Baidu Baike2 , a large-scale Chinese online encyclopedia , is used to construct the knowledge base ( KB ) . A snapshot of Baike containing 68,067 unique entities was collected on May 10th , 2017 . Four attributes are leveraged for feature engineering : ( 1 ) gender , ( 2 ) nicknames as a list of common aliases for an entity , ( 3 ) entity type , and ( 4 ) entity relation . 4 . The Proposed Approach . Our model takes as input a phrasal mention m in a comment C , which is posted under an article . Given a knowledge base , we aim to predict the KB entity that m refers to , or to label it as NIL if no such entity exists . Concretely , a list of candidate entities will be first selected as Em = { e } based on string matching and knowledge graph expansion ( see § 4.1 ) . Then a linking probability will be computed over each candidate given m . 4.1 Candidate Construction . Our candidate construction algorithm consists of two steps . For each mention , we consider all entities that appear in the same comment and corresponding article by matching their canonical names . This forms the initial candidate list . In the second step , a new entity 2. https : //baike.baidu.com is selected if it has a relation with any entity in the initial list according to our KB . The initial list , the expanded entities , and NIL comprise the final candidate set . Following this procedure , 96 % gold-standard entities are retrieved in the candidate sets for Ent , and 62 % are covered for Prod . To improve the coverage for the Prod domain , we collect unambiguous aliases ( no other entity with the same alias ) that are not pronominal mentions from training data for each entity , and use these as additional entity nicknames for candidate construction . The coverage is increased to 93 % .

Summary: This work presents a novel neural model, XREF, for entity linking in Chinese online news comments. Two new datasets of news articles and comments in entertainment and product domains are collected and annotated for evaluation on this task. The unique problem setup-up facilitates XREF to use the corresponding news article in the following ways: