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Text keywords at different semantic levels have different semantic representation abilities. Although words have been organized by semantic dictionaries (e.g. WordNet) with exact semantics, the dictionaries can not be constructed automatically by machine and there are still many words which are not included in the
Automatic image annotation is crucial for keyword-based image retrieval. There is a trend focusing on utilization of machine learning techniques, which learn statistical models from annotated images and apply them to generate annotations for unseen images. In this paper we propose MAGMA - new image auto-annotation
Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance on twitter rely on inflexible keyword based approaches that require
By conducting a word frequency statistics in the posing on Eastmoney forum bar about SSE Composite Index from January 7, 2010 to August 30, 2013, we establish a set of keyword dictionary to measure investor sentiment effectively, and accordingly to study the mutual relations between the abnormal investor sentiment
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.These exact word-matching and ranking approaches ignore too many relevant documents that do not contain the exact keywords as specified in a user query
student's answer against an answer-key that comprises key phrases of the solution and alternatives, if any. We rely on five algorithms from literature on natural language processing to assess various aspects of a student's answer, such as the quantity and extent of match between keywords in a student's answer and the answer
propose n keywords, in order to optimise the information gain expectation. Its implementation, CFAsT, endeavours to keep the best from both worlds: the universality and automatic generation from search engines, and the usability, the assistance and the self optimisation provided by the dialogue systems. Thus, a beta dialogue
utterance of manipulator through training. In experiments we test how many necessary keywords the outputs of traditional system and our system can cover respectively. Finally we ask volunteers to give scores to both systems for the sake of demonstrating satisfactions to their utterances.
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