Arabic Temporal Entity Extraction using Morphological Analysis
نویسنده
چکیده
The detection of temporal entities within natural language texts is an interesting information extraction problem. Temporal entities help to estimate authorship dates, enhance information retrieval capabilities, detect and track topics in news articles, and augment electronic news reader experience. Research has been performed on the detection, normalization and annotation guidelines for Latin temporal entities. However, research in Arabic lags behind and is restricted to commercial tools. This paper presents a temporal entity detection technique for the Arabic language using morphological analysis and a finite state transducer. It also augments an Arabic lexicon with 550 tags that identify 12 temporal morphological categories. The technique reports a temporal entity detection success of 94.6% recall and 84.2% precision, and a temporal entity boundary detection success of 89.7% recall and
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