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Register a new userNeural Sequence Segmentation as Determining the Leftmost Segments
تجزئة التسلسل العصبي كما تحديد القطاعات اليسرى
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الأساليب السابقة لتجزئة النص هي في الغالب على مستوى الرمز المميز.على الرغم من الكفاية، تحد هذه الطبيعة من إمكاناتها الكاملة لالتقاط التبعيات طويلة الأجل بين القطاعات.في هذا العمل، نقترح إطارا جديدا يزدر أدرج جمل اللغة الطبيعية في مستوى القطاع.لكل خطوة في تجزئة، يعترف الجزء الأكبر في أقصى اليسار من التسلسل المتبقي.تنطوي التطبيقات على تقنية LSTM-ناقص لبناء تمثيل العبارات والشبكات العصبية المتكررة (RNN) لنموذج تكرارات تحديد الأقصى اليمنى.لقد أجرينا تجارب واسعة النطاق على العلامات على الجزء العلوي من قطع البيانات والصينية (POS) عبر 3 مجموعات من مجموعات البيانات، مما يدل على أن أساليبنا تتفوق بشكل كبير على جميع خطوط الأساس السابقة وحققت نتائج جديدة من الفنادق الجديدة.علاوة على ذلك، فإن التحليل النوعي والدراسة حول تجزئة الجمل الطويلة الطويلة تحقق من فعاليته في نمذجة التبعيات طويلة الأجل.
Prior methods to text segmentation are mostly at token level. Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments. In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. For every step in segmentation, it recognizes the leftmost segment of the remaining sequence. Implementations involve LSTM-minus technique to construct the phrase representations and recurrent neural networks (RNN) to model the iterations of determining the leftmost segments. We have conducted extensive experiments on syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets, demonstrating that our methods have significantly outperformed previous all baselines and achieved new state-of-the-art results. Moreover, qualitative analysis and the study on segmenting long-length sentences verify its effectiveness in modeling long-term dependencies.
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