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Register a new userModeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model
النمذجة بنية بنية في الرسوم البيانية استخدام Word مع نموذج كتلة مؤشر الاستوكاستك الموزونة
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نقترح أن نقترح تصميم الرسوم البيانية التي تم تفكيكها عن الكلمات الفائقة من الفئة الدلالية الفائقة بين استخدامات الكلمات مع صياغة Bayesian لنموذج Black Block المرجح، وهو نموذج عام لرسوم بيانية عشوائية شعبية في علم الأحياء والفيزياء والعلوم الاجتماعية.من خلال توفير نموذج احتمامي للكلمة المتقدمة مما يعني أننا نهدف إلى الاقتراب من الفكرة الزلقة وحتى الآن استخدامها على نطاق واسع من معنى كلمة بطريقة جديدة.يتيح لنا الإطار المقترح من مقارنة نماذج Word بصرامة فيما يتعلق بملائمها للبيانات.نحن نؤدي تجارب واسعة وتحديد النموذج الأكثر كفاءة تجريبيا.
We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences. By providing a probabilistic model of graded word meaning we aim to approach the slippery and yet widely used notion of word sense in a novel way. The proposed framework enables us to rigorously compare models of word senses with respect to their fit to the data. We perform extensive experiments and select the empirically most adequate model.
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