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Neural-based context-aware models for slot tagging tasks in language understanding have achieved state-of-the-art performance, especially deep contextualized models, such as ELMo, ...
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[1812.10628] Intent Detection and Slots Prompt in a Closed-Domain Chatbot
In this paper, we introduce a methodology for predicting intent and slots of a query for a chatbot that answers career-related queries. We take a multi-staged approach where both the processes (intent-classification and slot-tagging) inform each other's decision-making in different stages. The model breaks down the problem into stages, solving one problem at a time and passing on relevant results of the current stage to the next, thereby reducing search space for subsequent stages, and eventually making classification and tagging more viable after each stage. We also observe that relaxing rules for a fuzzy entity-matching in slot-tagging after each stage (by maintaining a separate Named Entity Tagger per stage) helps us improve performance, although at a slight cost of false-positives. Our model has achieved state-of-the-art performance with F1-score of 77.63% for intent-classification and 82.24% for slot-tagging on our dataset that we would publicly release along with the paper.
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SfDay 2019: Head first into Symfony Cache, Redis & Redis Cluster | PPT
The document discusses the features and advantages of Symfony's caching system, focusing on the integration of Redis and the benefits of using cache tagging. It compares Redis and Memcached, highlighting Redis's capabilities with various data types and its support for clustering. The talk also addresses performance optimizations, particularly in Symfony 4.3, and gives insights into real-world caching strategies and edge cases encountered in application development. - Download as a PDF or view online for free
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spaCy · Industrial-strength Natural Language Processing in Python
spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.
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BKAI-NAVER Challenge 2022 là hoạt động thuộc dự án hợp tác nghiên cứu AI giữa Trung tâm BKAI, Trường CNTT&TT và Tập đoàn Naver (Hàn Quốc). Dữ liệu cung cấp trong cuộc thi là một phần kết quả trong các dự án hợp tác năm 2021 giữa hai bên. Cuộc thi gồm ba tác vụ: Trích xuất ý định và slot trong câu lệnh người dùng (Vietnamese Intent Detection and Slot Tagging); Khoanh vùng cơ thể và nhận dạng cử chỉ động (Body Segmentation and Gesture Recognition); Phát hiện và nhận diện văn bản Tiếng Việt trong ảnh khung cảnh (Vietnamese Scene Text Detection and Recognition).