图 书 馆
TruePIE：在基于模式的信息提取中发现可靠模式TruePIE: Discovering Reliable Patterns in Pattern‑Based Information Extraction |
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| 课程网址: | http://videolectures.net/kdd2018_li_truepie_patterns/ |
| 主讲教师: | Qi Li |
| 开课单位: | 伊利诺伊大学香槟分校计算机科学系 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Pattern-based methods have been successful in information extraction and NLP research. Previous approaches learn the quality of a textual pattern as relatedness to a certain task based on statistics of its individual content (e.g., length, frequency) and hundreds of carefully-annotated labels. However, patterns of good content-quality may generate heavily conflicting information due to the big gap between relatedness and correctness. Evaluating the correctness of information is critical in (entity, attribute, value)-tuple extraction. In this work, we propose a novel method, called TruePIE, that finds reliable patterns which can extract not only related but also correct information. TruePIE adopts the self-training framework and repeats the training-predicting-extracting process to gradually discover more and more reliable patterns. To better represent the textual patterns, pattern embeddings are formulated so that patterns with similar semantic meanings are embedded closely to each other. The embeddings jointly consider the local pattern information and the distributional information of the extractions. To conquer the challenge of lacking supervision on patterns’ reliability, TruePIE can automatically generate high quality training patterns based on a couple of seed patterns by applying the arity-constraints to distinguish highly reliable patterns (i.e., positive patterns) and highly unreliable patterns (i.e., negative patterns). Experiments on a huge news dataset (over 25GB) demonstrate that the proposed TruePIE significantly outperforms baseline methods on each of the three tasks: reliable tuple extraction, reliable pattern extraction, and negative pattern extraction. |
| 关 键 词: | 信息提取和NLP研究; 精心标注的标签的统计数据; TruePIE; 可靠元组提取 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-27：cyh |
| 最后编审: | 2023-01-27：cyh |
| 阅读次数: | 45 |