Selecting Key Views for Zero-Shot Entity Linking (2023)

Abstract: Entity linking, which aligns mentions in the text to entities in knowledge bases, is essential for many natural language processing tasks. Considering the real-world scenarios, recent research hotspot of entity linking has focused on the zero-shot setting, where mentions need to link to unseen entities and only the description of each entity is provided. This task challenges the language understanding ability of models to capture the coherence evidence between the mention context and entity description. However, entity descriptions often contain rich information from multiple views, and a mention with context only relates to a small part of the information. Other irrelevant information will introduce noise, which interferes with models to make the right judgments. Furthermore, the existence of these information also makes it difficult to synthesize key information. To solve these problems, we select key views from descriptions and propose a KVZEL framework for zero-shot entity linking. Specifically, our KVZEL first adopts unsupervised clustering to form sub views. Then, it employs a mention-aware key views selection module to iteratively accumulate mention-focused views. This puts emphasis on capturing mention-related information and allows long-range key information integration. Finally, we aggregate key views to make the final decision. Experimental results show the effectiveness of our KVZEL and it achieves the new state-of-the-art on the zero-shot entity linking dataset.

## introduction entity linking (el) is the task of assigning ambiguous mentions in textual input to their corresponding entities in a given real-world knowledge base. el, as a fundamental task in the information extraction area, plays an important role in many downstream natural language processing (nlp) applications, * corresponding author. lebron james lebron raymone james sr. is an american professional basketball player of the national basketball association (nba)… james married his high school sweetheart savannah james. they have three children: two sons-bronny and bryce-and daughter zhuri… james and his business partner maverick carter own production company springhill entertainment… he has won 19 espy awards , hosted saturday night live , and starred in the sports film space jam: a new legacy ( 2021 figure 1 : the motivation illustration of our proposed framework. usually, an entity can be linked by different mentions from multiple views. our proposed framework comes from this observation and selects key views from entity descriptions to better align mentions with divergent context. to address this, the zero-shot entity linking task @xcite has been proposed, where mentions need to be linked to unseen entities of new domains. moreover, considering that there is no external knowledge (e.g. frequency statistics and structured data) in many specialized domains, the textual description for each entity is the only information provided in this task. based on the above, we can see that the entity description, as the only identification information of entities, is crucial for this task. some previous works @xcite have realized its importance and constructed models to circumvent the length limitation problem of bert-based models to capture richer entity description information. however, to the best of our knowledge, all previous methods utilize the same entire entity description to match different mentions with divergent context. this seems struggling to manage entities with rich multiple views information. we argue that only part of description information is related to mentions and other irrelevant information will introduce noise and make models reduce emphasis on the corresponding details. an example shown in figure 1a , the entity description of "lebron james" contains multiple views information, such as his nba journey, his family, etc. for the first mention, it only relates to "view 1" in the entity description. other views are useless in determining whether the "james" should link to the entity "le-bron james". for the second mention, it is related to "view 2" and "view 4". however, the description of "lebron james" mainly concentrates on his professional journey, which will introduce noise to interfere with entity linking models matching "his father" and "lebron james". furthermore, "view 2" and "view 4" may exist in different parts of the description, which makes it not trivial to synthesize the key information of the two views. to address these problems, in this paper, we try to select key views from entity descriptions for different mentions with divergent context, which is shown in figure 1b . this can also help entity linking models solve the problem of length limitation in bert-based models, since the selected views are only a part of entity description. we propose a novel key views selection framework for zero-shot entity linking in this paper, kvzel in short. kvzel first adopts unsupervised clustering to group the sentences, and those in each cluster compose a sub view. when judging whether a mention in text matches the entity, humans tend to read the whole description and only focus on the view that is most relevant to the mention. considering that this mention may be related to multiple views of entity description, after finding that a single view is insufficient to make the judgment, humans will use the previously relevant views as clues and move on to find the next relevant view. with the inspiration of the human's thinking process, our kvzel designs a mention-aware key views selection module to iteratively accumulate the most relevant views. these views are then aggregated into a new text, which serves as the mention-focused and highly-condensed version of original entity description. finally, the text replaces the description and is utilized by our entity linking module to make the final decision. to summarize, our major contributions are shown as follows: ## methodology figure 2 shows the overall architecture of our proposed kvzel framework. we first use the sub view clustering module to generate sub views from descriptions. then we design a mention-aware key views selection module to select relevant sub views for different mentions with divergent context. finally, our entity linking module makes the final decision based on the selected views. in this section, we introduce these three modules in detail. ## analysis 5.1 ablation study to better understand our kvzel framework, we investigate the contribution of some components through ablation studies, and the results are also shown in ## conclusion in this paper, we explore an issue that was overlooked when only textual descriptions are available in zero-shot entity linking scenarios, that is, only part of description information is related to mentions and other irrelevant information will introduce noise and make models reduce emphasis on the corresponding details. to overcome this issue, we propose a kvzel framework to select key views from entity descriptions for different mentions with divergent context. specifically, it first adopts unsupervised clustering to form sub views. then, it imitates the human's thinking process to iteratively accumulate mention-related views, which helps our entity linking module have the ability of long-range information integration. the selected views are aggregated into a new text, which replaces the original description as the input of our entity linking module to make the final decision. experimental results demonstrate that the proposed kvzel framework achieves new state-of-the-art performance on the zero-shot entity linking dataset.