Text Chunking Using Transformation-Based Learning
Lance Ramshaw,Mitchell Marcus +1 more
- pp 157-176
TLDR
This work has shown that the transformation-based learning approach can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks.Abstract:
Transformation-based learning, a technique introduced by Eric Brill (1993b), has been shown to do part-of-speech tagging with fairly high accuracy. This same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 93% for baseNP chunks (trained on 950K words) and 88% for somewhat more complex chunks that partition the sentence (trained on 200K words). Working in this new application and with larger template and training sets has also required some interesting adaptations to the transformation-based learning approach.read more
Citations
More filters
Proceedings ArticleDOI
A Simple Method for Resolution of Definite Reference in a Shared Visual Context
TL;DR: This article presented a method for resolving definite exophoric reference to visually shared objects that is based on a simple mapping of words to visual features, an automatically learned, semantically-motivated utterance segmentation, and a procedure that, given an utterance, uses b) to combine a) to yield a resolution.
A Character n-gram Based Approach for Improved Recall in Indian
TL;DR: The authors applied the same technique on Indian Languages, and experimented with Conditional Random Fields (CRFs), a discriminative model, and evaluated their system on two Indian Languages Telugu and Hindi.
Proceedings ArticleDOI
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Elsbeth Turcan,Shuai Wang,Rishita Anubhai,Kasturi Bhattacharjee,Yaser Al-Onaizan,Smaranda Muresan +5 more
TL;DR: This work proposes novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging and shows performance improvement on both tasks when including common- sense reasoning and a multitask framework.
Rapid Development of NLP Modules with Memory-based Learning
TL;DR: This work demonstrates that the three modules trained with MBL display high generalization accuracy, and argues why MBL is applicable similarly well to a large class of other NLP tasks.
Proceedings ArticleDOI
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
Di Wu,Liang Ding,Fan Lu,Jian Xie +3 more
TL;DR: The authors proposed a non-autoregressive model named SlotRefine for joint intent detection and slot filling, and designed a two-pass iteration mechanism to handle the uncoordinated slots problem.
References
More filters
Book ChapterDOI
Parsing By Chunks
TL;DR: The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template, and the relationships between chunks are mediated more by lexical selection than by rigid templates.
Proceedings ArticleDOI
A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text
TL;DR: The authors used a linear-time dynamic programming algorithm to find an assignment of parts of speech to words that optimizes the product of (a) lexical probabilities (probability of observing part of speech i given word i) and (b) contextual probabilities (pb probability of observing n following partsof speech).
Proceedings Article
Some advances in transformation-based part of speech tagging
TL;DR: In this article, a rule-based approach to tagging unknown words is described, where the tagger-can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
Journal ArticleDOI
Performance structures: A psycholinguistic and linguistic appraisal☆
James Paul Gee,François Grosjean +1 more
TL;DR: In this paper, two lines of research are combined to deal with a long-standing problem in both fields: why the performance structures of sentences (structures based on experimental data, such as pausing and parsing values) are not fully accountable for by linguistic theories of phrase structure.
Book
A corpus-based approach to language learning
TL;DR: A learning algorithm is described that takes a small structurally annotated corpus of text and a larger unannotated corpus as input, and automatically learns how to assign accurate structural descriptions to sentences not in the training corpus.