Event Centric Entity Linking for H indi News Articles: A Knowledge Graph Based Approach (2019)

Abstract: We describe the development of a knowledge graph from an event annotated corpus by presenting a pipeline that identifies and extracts the relations between entities and events from Hindi news articles. Due to the semantic implications of argument identification for events in Hindi, we use a combined syntactic argument and semantic role identification methodology. To the best of our knowledge, no other architecture exists for this purpose. The extracted combined role information is incorporated in a knowledge graph that can be queried via subgraph extraction for basic questions. The architectures presented in this paper can be used for participant extraction and event-entity linking in most Indo-Aryan languages, due to similar syntactic and semantic properties of event arguments.

## introduction events are defined as situations that happen or occur @xcite . events therefore involve participating entities, sometimes referred to as event arguments @xcite . the extraction of role information of entities participating in events is a fast-evolving area of research in information retrieval as well as subfields of nlp such as question answering and summarization @xcite . this paper handles the challenge of participant detection and labeling in hindi, using syntactic measures such as dependency parsing and semantic measures such as verb frame comparisons and semantic role labeling. using the entities extracted from the text and their relation to the event, a knowledge graph is generated, which can then be queried for basic questions. in hindi nlp, the representation, identification and extraction of events is a fairly new concept. event extraction from twitter data @xcite and in news data @xcite are still developing areas of research. however, extensive work has been done on argument structure for hindi verbs, therefore the syntactic analysis of verbal events has been a topic of sufficient inquiry @xcite . on the other hand, nominal events, while not studied under that paradigm, have been referenced in entity linking in ner research @xcite . this paper, given the definition of events in hindi @xcite , identifies the arguments of these events. we employ a syntactico-semantic approach of entity identification by using dependency parsing to determine the syntactic roles of the arguments and their dependency length from the event mention @xcite , and a semantic role labeler which is used to determine the semantic case or functions of the participating entities @xcite . for verbal events, verb frame data has also been used for verifying the arguments. this information is constructed as a knowledge graph, a query graph @xcite of which can then be used for question answering. ## related work entity or participant extraction is a vital subdomain of event detection and related information extraction tasks. the ace project @xcite and many of the relevant event extraction tasks that followed it had entity detection and tracking as one of the main components for event detection and extraction systems @xcite . ace also provided twenty-four different types of relations between entities. @xcite establishes a mechanism of using entity links in order to more accurately detect event mentions, by associating some entities as event par-ticipants or arguments. joint extraction of event and entity mentions has been attempted @xcite by learning intra-event structures and possible forms of entity relations to events. named entity recognition has been another broader form of approach to entity identification and linking. entity mention detection and tracking its use in the corpus @xcite is considered the most fundamental method in this approach. @xcite approaches the problem of named entity recognition from the perspective of entity linking. hybrid joint approaches to participant extraction and linking @xcite have been treated as an extension of this problem, and the oke 2017 task @xcite performed participant extraction and linking for ontology enrichment. @xcite and @xcite perform cross lingual entity linking over an enriched knowledge base, one of the languages being hindi. these approaches are important for understanding and disambiguating the links between nominal events and their participants. argument analysis for verbs in hindi has been a well-researched topic, as mentioned above. @xcite studies the computational properties of verbal predicates from a dependency annotation perspective, while @xcite and @xcite focuses on the syntactic argument structure of light verbs in hindi. light verbs are one of the syntactic constructions observed in representation of eventive verbs. compound verb detection @xcite , complex predicate detection @xcite and argument identification in complex predicates @xcite can be modeled together in syntactic argument detection for verbal events. the study of noun incorporation in verb complexes @xcite provide a semantic perspective of argument structure and event participation. syntactically, two major concerns of verb argument analysis are verb phrase ellipsis and complex predicate analysis @xcite . knowledge graphs are extensively used in semantic information retrieval and has numerous other applications cross language document retrieval @xcite , cross lingual plagiarism detection @xcite , question answering @xcite and summarization @xcite . ## dataset and annotation specifications we use a gold-standard corpus of 810 news articles of @xcite , and annotate it for entities and their relations with the events. the entityevent relations are annotated based on a syntactic as well as a semantic role. the syntactic role is simply a dependency label @xcite , while the semantic labels are provided according to hindi and urdu propbank labels @xcite . the annotated sentence shown in figure 1 is: the dataset is then annotated further for ease of semantic role extraction. all event mentions are indexed, and if two event mentions are coreferent, they are given the same index. in case of entities, only entities with coreferent mentions are indexed. coreferent entity mentions are given the same index. for this task, inter-annotator agreement was calculated on four different measures, identifying the participant, correct syntactic role, correct semantic role and correct coreferent mention identification. for the purpose of coreference, the entities and events are treated the same. ## identifying entity participation in events in this section, we look at the pipeline for the extraction of entities as arguments of events. we define here a nominal event as an event which has the event nugget (the core of the event) is a noun. similarly, a verbal event is an event which has the event nugget which is a verb. as discussed before, discerning entity participation in events is a syntacto-semantic problem, and therefore our solution (refer to figure 2 ) has both syntactic and semantic components. note that a iob (inside-outside-begin) tagged event mentioned corpus is the input to the pipeline. the outputs from each module and the final pipeline are formatted to be used in the form of a knowledge graph, which is detailed in section 5. ## entity-event knowledge graph knowledge graphs have been widely used in information retrieval, since their adoption in popular search engines. however, knowledge graphs can be constructed for document wide, corpus wide or domain wide extraction of information as well. in this section, we show the development of an eventcentric entity linked knowledge graph. @xcite defines an event centric knowledge graph as a knowledge graph in which all information is related to events through which the knowledge in the graph obtains a temporal dimension. knowledge graphs are useful for the representation of semantic information in the edge labels or in the attributes of the nodes itself. document wide knowledge graphs can be queried by limiting the search space based on the query. this method of creating a query graph allows for an inference chain for the related nodes @xcite . question word gloss category role kis + case marker 2 who entity kaun who entity -kahan where entity location or source kab when entity (time) kyun why event or entity (goal) ## analysis and results in this section, we look into the two pipelines which have been developed for constructing a basic knowledge graph from an event annotated corpus, and the type of queries it can handle. we provide both a qualitative and quantitative analysis of the results of the pipelines. we also provide a thorough analysis of errors. ## conclusion in this paper, we attempted to determine a method of identifying the participants of each event in an event-annotated corpus, given the syntactic and semantic role of each noun and verb in a sentence. we used a distinct pipeline of interacting tools which provided various levels of syntactic and semantic information, which were then combined and analyzed. we have presented the two major algorithms; one for identifying the sense of the verb being used (based on the available mandatory arguments and the maximum match of optional arguments), and the other for determining the participants of a nominal event. we have also presented the development of a queryable knowledge graph on the basis of the events and entities extracted, that use the role information as edge labels. with this work, we hope to develop a more robust representation of events and entities, which can be enriched with developments in event classification and event relation extraction in hindi. most importantly, the pipeline and algorithms developed in this paper are language agnostic, which we hope will spur research into developing information rich representations of event and participation information in other languages as well.