skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the imperative need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,641 annotations labeled at the sentence level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape as well as inferring entity ideology.  more » « less
Award ID(s):
2127747
PAR ID:
10518848
Author(s) / Creator(s):
; ;
Publisher / Repository:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Date Published:
Page Range / eLocation ID:
9950 to 9969
Format(s):
Medium: X
Location:
Abu Dhabi, United Arab Emirates
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers))
    null (Ed.)
    This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research. 
    more » « less
  2. ; ; ; ; (, Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21))
    null (Ed.)
    Inferring the set name of semantically grouped entities is useful in many tasks related to natural language processing and information retrieval. Previous studies mainly draw names from knowledge bases to ensure high quality, but that limits the candidate scope. We propose an unsupervised framework, AutoName, that exploits large-scale text corpora to name a set of query entities. Specifically, it first extracts hypernym phrases as candidate names from query-related documents via probing a pre-trained language model. A hierarchical density-based clustering is then applied to form potential concepts for these candidate names. Finally, AutoName ranks candidates and picks the top one as the set name based on constituents of the phrase and the semantic similarity of their concepts. We also contribute a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all compared methods. Further analyses show that AutoName is able to offer explanations for extracted names using the sentences most relevant to the corresponding concept. 
    more » « less
  3. ; ; ; ; ; ; ; (, Findings of the Association for Computational Linguistics: NAACL 2022)
    We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment. 
    more » « less
  4. ; (, Springer)
    Knowledge Graph embeddings model semantic and struc- tural knowledge of entities in the context of the Knowledge Graph. A nascent research direction has been to study the utilization of such graph embeddings for the IR-centric task of entity ranking. In this work, we replicate the GEEER study of Gerritse et al. [9] which demonstrated improvements of Wiki2Vec embeddings on entity ranking tasks on the DBpediaV2 dataset. We further extend the study by exploring additional state-of-the-art entity embeddings ERNIE [27] and E-BERT [19], and by including another test collection, TREC CAR, with queries not about person, location, and organization entities. We confirm the finding that entity embeddings are beneficial for the entity ranking task. Interestingly, we find that Wiki2Vec is competitive with ERNIE and E-BERT. Our code and data to aid reproducibility and further research is available at https://github.com/poojahoza/E3R-Replicability 
    more » « less
  5. ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a "domain" is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities. We derive a large inventory of types (tens of thousands) from Wikipedia categories, and use hyperlinked mentions in Wikipedia to distantly label data and train an entity typing model. At test time, we classify a mention with this typing model and use soft type predictions to link the mention to the most similar candidate entity. We evaluate our entity linking system on the CoNLL-YAGO dataset and show that our approach outperforms prior domain-independent entity linking systems. We also test our approach in a harder setting derived from the WikilinksNED dataset where all the mention-entity pairs are unseen during test time. Results indicate that our approach generalizes better than a state-of-the-art neural model on the dataset. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email