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Register a new userLearning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph
تعلم الحساب: نهج تضمين بسيط ولكنه فعال باستخدام الرسم البياني المعرفة
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تلعب الحساب دورا رئيسيا في فهم اللغة الطبيعية.ومع ذلك، فإن نهج NLP الحالية، وليس فقط نهج Word2VEC التقليدي أو نماذج اللغة المستندة إلى المحولات السياقية، تفشل في تعلم الحساب.ونتيجة لذلك، فإن أداء هذه النماذج محدود عند تطبيقه على التطبيقات المكثفة في المجالات السريرية والمالية.في هذا العمل، نقترح نهج تضمين عدد بسيط بناء على الرسم البياني للمعرفة.نحن نبني رسم بياني للمعرفة يتكون من كيانات الأرقام وعلاقات الحجم.يتم بعد ذلك تطبيق طريقة تضمين الرسم البياني للمعرفة للحصول على ناقلات الرقم.نهجنا سهل التنفيذ، وتجربة نتائج التجربة على مختلف مهام NLP ذات الصلة بالكمال إظهار فعالية وكفاءة طريقتنا.
Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number entities and magnitude relations. Knowledge graph embedding method is then applied to obtain number vectors. Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.
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Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events
in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
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Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational c
ost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
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