Evaluating Automated and Manual Acquisition of Anaphora Resolution Strategies
Summary
The paper tackles anaphora resolution, a persistent bottleneck for applications such as information extraction and machine translation. Real systems must cope with noisy upstream analysis and with anaphoric phenomena that vary by language and domain. The authors previously built a Manually-Designed Resolver (MDR) that relies on hand-selected, hand-ordered discourse knowledge sources. Here they build a Machine Learning-based Resolver (MLR) and compare the two. They annotated Japanese newspaper articles about joint ventures with coreference links using their GUI annotation tool, DTTool. They then trained C4.5 decision trees on feature vectors that each describe an anaphor paired with a candidate antecedent. Several MLR variants beat the hand-built system, and with only 50 training texts the learned resolver already surpasses the MDR. Decision trees were chosen partly because they are readable, so the features the trees select can be compared with linguistic theories of Japanese zero pronouns. The work suggests that annotated corpora can replace costly manual engineering of discourse preferences.
Key points
- Annotation scheme: The corpus uses a detailed SGML tag set covering definite NPs (prefixed by "dou", "ryou", "kono" or "sono"), proper-name anaphora (NAME), reflexives, and indexicals. It also covers zero pronouns, split into ZPRO and QZPRO, with semantic subtypes such as ORG and P.
- Features: The trees use 66 lexical, syntactic, semantic and positional features, both unary and binary. These are computed automatically from the system's discourse markers, so some feature values are noisy.
- Training variants: Three settings were varied:
- whether all pairs on the anaphoric chain (transitive closure) count as positive examples;
- whether the tree predicts the anaphoric type or only a yes/no answer;
- the pruning confidence factor (25, 50, 75 or 100%).
- Learning curve: Performance levels off at an F-measure of about 77.4 once roughly 250 training examples are used.
- Weak spots: Resolution of ZPRO-ORG and DNP-ORG is weaker, likely because the batch-mode MLR cannot pass along links it has already resolved. For QZPRO-ORG, the trees relied on topicalization, distance, semantic class and subjecthood, which partly overlaps with the ranking proposed by Walker et al.
Cited by
- Anaphoric Zero Pronoun Identification: A Multilingual Approach
- Cross-lingual Zero Pronoun Resolution
- Bootstrapping Path-Based Pronoun Resolution
- Noun Phrase Coreference as Clustering
- Japanese Zero Pronoun Resolution based on Ranking Rules and Machine Learning
- Probabilistic Coreference in Information Extraction
- Employing the Centering Theory in Pronoun Resolution from the Semantic Perspective
- Rule-based Coreference Resolution in German Historic Novels
- Using the Web in Machine Learning for Other-Anaphora Resolution
- Aiding Pronoun Translation with Co-Reference Resolution
- Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules
- Improving Machine Learning Approaches to Coreference Resolution
- Machine Learning for Coreference Resolution: From Local Classification to Global Ranking
- Supervised Noun Phrase Coreference Research: The First Fifteen Years
- Creating a Coreference Resolution System for Italian
- A Global Relaxation Labeling Approach to Coreference Resolution
- A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution
- A Probabilistic Method for Analyzing Japanese Anaphora Integrating Zero Pronoun Detection and Resolution
- A Machine Learning Approach to Coreference Resolution of Noun Phrases
- Coreference Systems Based on Kernels Methods
- Coreference Resolution Using Competition Learning Approach
- Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge
- An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming
- A Twin-Candidate Model for Learning-Based Anaphora Resolution
- Context-Sensitive Convolution Tree Kernel for Pronoun Resolution
Cite
@inproceedings{aone1995evaluating,
title = {{Evaluating Automated and Manual Acquisition of Anaphora Resolution Strategies}},
author = {Aone, Chinatsu and Bennett, Scott William},
booktitle = {33rd Annual Meeting of the Association for Computational Linguistics},
year = {1995},
doi = {10.3115/981658.981675},
url = {https://doi.org/10.3115/981658.981675}
}References
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