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引用次数: 0
摘要
信息提取(IE)侧重于将非结构化数据转化为结构化知识,而命名实体识别(NER)是其中的一个基本组成部分。在信息检索(IR)领域,有效识别实体可以大大提高搜索和推荐系统的精确度。现有的方法将 NER 定义为序列标注任务,这需要额外的数据,因此在可持续性方面可能受到限制。一个有前景的解决方案是采用机器阅读理解(MRC)方法来完成 NER 任务,从而消除对额外数据的依赖。这一过程会遇到一些关键挑战,包括1) 非常规预测;2) 多流处理效率低下;3) 缺乏熟练的推理策略。为此,我们提出了单流推理器(SSR),这是一种利用推理策略和标准化输入的解决方案。这为平面和嵌套 NER 任务提供了一种类型无关的解决方案,而无需额外的数据。在十个 NER 基准上,SSR 取得了最先进的结果,凸显了它的鲁棒性。此外,我们还通过收敛性、推理速度和低资源场景性能比较说明了它的效率。我们的架构具有很强的适应性,可以毫不费力地与各种基础模型和推理策略融合,从而促进了 IR 和 IE 领域的进步。
SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner
Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data and, therefore may be limited in terms of sustainability. One promising solution is to employ a Machine Reading Comprehension (MRC) approach for NER tasks, thereby eliminating the dependence on additional data. This process encounters key challenges, including: 1) Unconventional predictions; 2) Inefficient multi-stream processing; 3) Absence of a proficient reasoning strategy. To this end, we present the Single-Stream Reasoner (SSR), a solution utilizing a reasoning strategy and standardized inputs. This yields a type-agnostic solution for both flat and nested NER tasks, without the need for additional data. On ten NER benchmarks, SSR achieved state-of-the-art results, highlighting its robustness. Furthermore, we illustrated its efficiency through convergence, inference speed, and low-resource scenario performance comparisons. Our architecture displays adaptability and can effortlessly merge with various foundational models and reasoning strategies, fostering advancements in both IR and IE fields.
期刊介绍:
The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain:
new principled information retrieval models or algorithms with sound empirical validation;
observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking;
accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques;
formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks;
development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking;
development of computational models of user information preferences and interaction behaviors;
creation and analysis of evaluation methodologies for information retrieval and information seeking; or
surveys of existing work that propose a significant synthesis.
The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.