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Register a new userFine-grained Typing of Emerging Entities in Microblogs
غرامة الكتابة الجميلة للكيانات الناشئة في المدونات الصغيرة
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تحليل المدونات الصغيرة حيث ننشر ما نقوم بتمكيننا من أداء تطبيقات مختلفة مثل تحليل الاتجاه الاجتماعي وتوصية الكيان. لتتبع الاتجاهات الناشئة في مجموعة متنوعة من المجالات، نريد تصنيف المعلومات عن الكيانات الناشئة (على سبيل المثال، الصورة الرمزية 2) في منشورات المدونات الصغيرة وفقا لأنواعها (على سبيل المثال، فيلم). وبالتالي، فإننا نقدم مهمة جديدة للكتابة كيان تقوم بتعيين نوعا غرامة على كل كيان ناشئ عند إجراء انفجار من المشاركات التي تحتوي على هذا الكيان لأول مرة في المدونات الصغيرة. التحدي هو إجراء الكتابة من منشورات المدونات الصغيرة الصاخبة دون الاعتماد على المعرفة السابقة للكيان المستهدف. لمعالجة هذه المهمة، نبني مجموعات بيانات Twitter واسعة النطاق للغة الإنجليزية واليابانية باستخدام الإشراف البعيد الحساسة للوقت. ثم نقترح نموذج الكتابة العصبي المعياري الذي لا ينفدي فقط الكيان وسياقاته ولكن أيضا معلومات المعتوه في مشاركات متعددة. لكتابة الكيانات الناشئة "الكتاني" تعني لغة البرمجة الناشئة ولعبة الألواح الكلاسيكية)، والسياقات الكلاسيكية صاخبة، ونطير محدد سياق يجد سياقات ذات صلة للكيان الهدف. تؤكد التجارب على مجموعات البيانات Twitter فعالية نموذج الكتابة لدينا ومنح السياق.
Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type homographic' emerging entities (e.g., Go' means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
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Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic
and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
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Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web r
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Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this iss
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