Online News Article Temporal Phrase Extraction for Causal Linking
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
This research proposes a method to detect temporal links between news articles using phrase extraction and position analysis. The words from the information feed were split into tokens using special character as the delimiters. The words, symbols and tokens were marked by the type of word such as noun, verb, adjective, preposition and adverb. The position of each word and its length and distance from the verb on the either side of previous 2 and next 2 sentences was extracted as a feature. The time based relations between several sentences using noun as the root and verbs as the action event was stored. The link length and hop count was measured and the connected tree was stored as a decision model. The prediction model was evaluated using time based questions and event series queries. The results showed 67% accuracy in terms of correct event sequencing and recall rate. In the next step emotion based classification of the news article event actions was performed to test whether the weighted graph linking based on phrase position properties (length, count, location) method could be used for sentiment/emotion analysis with fear and happiness as the class labels. This evaluation showed clear thresholds in case of specific event-event and event-action pairs and their temporal relation type.
Authors and Affiliations
Amol S Patwardhan
Keywords
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