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1. Indirect Supervision for Relation Extraction using Question-Answer Pairs

   https://doi.org/10.1145/3159652.3159709  

   Wu, Zeqiu; Ren, Xiang; Xu, Frank F.; Li, Ji; Han, Jiawei (February 2018, Proceedings of the Eleventh {ACM} International Conference on Web Search and Data Mining, {WSDM} 2018,)

   Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. However, current open IE systems ignore the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems. 

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2. Automatic Synonym Discovery with Knowledge Bases

   https://doi.org/10.1145/3097983.3098185  

   Qu, Meng; Ren, Xiang; Han, Jiawei (August 2017, Proceedings of the 23rd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining)

   Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., “apple” could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as “distant” supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework. 

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3. GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction

   https://doi.org/10.18653/v1/2022.findings-naacl.128  

   Wang, Yiwei; Chen, Muhao; Zhou, Wenxuan; Cai, Yujun; Liang, Yuxuan; Hooi, Bryan (January 2022, Findings of the Association for Computational Linguistics: NAACL 2022)

   Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance. We propose the GraphCache (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. GraphCache aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences. The global property features act as dataset-level prior knowledge for RE, and a complement to the sentence-level features. Inspired by the classical caching technique in computer systems, we develop GraphCache to update the property representations in an online manner. Overall, GraphCache yields significant effectiveness gains on RE and enables efficient message passing across all sentences in the dataset. 

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4. An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

   https://doi.org/10.18653/v1/2020.findings-emnlp.270  

   Swarup, Daivik; Bajaj, Ahsaas; Mysore, Sheshera; O’Gorman, Tim; Das, Rajarshi; McCallum, Andrew (November 2020, Findings of the Association for Computational Linguistics: EMNLP 2020)

   null (Ed.)

   In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus. 

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5. Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

   https://doi.org/10.1145/3178876.3186024  

   Qu, Meng; Ren, Xiang; Zhang, Yu; Han, Jiawei (April 2018, Proceedings of the 2018 World Wide Web Conference on World Wide Web, {WWW} 2018,)

   Extracting relations from text corpora is an important task with wide applications. However, it becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract from corpora more instances of the same relation. Existing distributional approaches leverage the corpuslevel co-occurrence statistics of entities to predict their relations, and require a large number of labeled instances to learn effective relation classiffers. Alternatively, pattern-based approaches perform boostrapping or apply neural networks to model the local contexts, but still rely on a large number of labeled instances to build reliable models. In this paper, we study the integration of distributional and pattern-based methods in a weakly-supervised setting such that the two kinds of methods can provide complementary supervision for each other to build an effective, uniffed model. We propose a novel co-training framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-conffdent instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion with text corpora and corpus-level relation extraction. Experimental results prove the effectiveness of our framework over many competitive baselines. 

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