Unsupervised Learning Helps Supervised Neural Word Segmentation
نویسندگان
چکیده
منابع مشابه
Unsupervised Segmentation Helps Supervised Learning of Character Tagging for Word Segmentation and Named Entity Recognition
This paper describes a novel character tagging approach to Chinese word segmentation and named entity recognition (NER) for our participation in Bakeoff-4.1 It integrates unsupervised segmentation and conditional random fields (CRFs) learning successfully, using similar character tags and feature templates for both word segmentation and NER. It ranks at the top in all closed tests of word segme...
متن کاملProsodic boundary information helps unsupervised word segmentation
It is well known that prosodic information is used by infants in early language acquisition. In particular, prosodic boundaries have been shown to help infants with sentence and wordlevel segmentation. In this study, we extend an unsupervised method for word segmentation to include information about prosodic boundaries. The boundary information used was either derived from oracle data (handanno...
متن کاملFigure-Ground Image Segmentation Helps Weakly-Supervised Learning of Objects
Given a collection of images containing a common object, we seek to learn a model for the object without the use of bounding boxes or segmentation masks. In linguistics, a single document provides no information about location of the topics it contains. On the contrary, an image has a lot to tell us about where foreground and background topics lie. Extensive literature on modelling bottom-up sa...
متن کاملNeural Word Segmentation Learning for Chinese
Most previous approaches to Chinese word segmentation formalize this problem as a character-based sequence labeling task so that only contextual information within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we propose a novel neural framework which thoroughly eliminates context windows and can utilize complete segmentation history. Ou...
متن کاملUnsupervised Word Segmentation Without Dictionary
This prototype system demonstrates a novel method of word segmentation based on corpus statistics. Since the central technique we used is unsupervised training based on a large corpus, we refer to this approach as unsupervised word segmentation. The unsupervised approach is general in scope and can be applied to both Mandarin Chinese and Taiwanese. In this prototype, we illustrate its use in wo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33017200