A Comparison of Structural Correspondence Learning and Self-training for Discriminative Parse Selection
نویسنده
چکیده
This paper evaluates two semi-supervised techniques for the adaptation of a parse selection model to Wikipedia domains. The techniques examined are Structural Correspondence Learning (SCL) (Blitzer et al., 2006) and Self-training (Abney, 2007; McClosky et al., 2006). A preliminary evaluation favors the use of SCL over the simpler self-training techniques.
منابع مشابه
Self-training a Constituency Parser using n-gram Trees
In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser. We approach the self-training problem with the assumption that while the full sentence parse tree produced by a parser may contain errors, some portions of it are more likely to be correct. We hypothesize that instead of feeding the parser the guessed full sentence parse trees of its own, we...
متن کاملLatent Structure Discriminative Learning for Natural Language Processing
Natural language is rich with layers of implicit structure, and previous research has shown that we can take advantage of this structure to make more accurate models. Most attempts to utilize forms of implicit natural language structure for natural language processing tasks have assumed a pre-defined structural analysis before training the task-specific model. However, rather than fixing the la...
متن کاملComparison Lecturing Method And Self-Learning on Knowledge of General Practitioners Participating in Continuing Education Course for Irritable Bowel Syndrome
Introduction: The aim of continuing medical education (CME) is to enhance knowledge and improves performance. Various ways is used in continuing education training. Stay away from work to participate in retraining will make problems for physicians and patients. Use the self-learning program might be a good way of continuing education. This study designed to compare training with lecturing metho...
متن کاملAdvances in Discriminative Parsing
The present work advances the accuracy and training speed of discriminative parsing. Our discriminative parsing method has no generative component, yet surpasses a generative baseline on constituent parsing, and does so with minimal linguistic cleverness. Our model can incorporate arbitrary features of the input and parse state, and performs feature selection incrementally over an exponential f...
متن کاملUnsupervised Parse Selection for HPSG
Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce the treebank, parse selection is not possible. Furthermore, as t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009