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- 540 creator furu-wei.
- 540 creator peng-zhang.
- 540 creator qin-lu.
- 540 creator wenjie-li.
- 540 creator yuexian-hou.
- 540 type InProceedings.
- 540 label "Exploiting the Role of Position Feature in Chinese Relation Extraction".
- 540 sameAs 540.
- 540 abstract "Relation extraction is the task of finding pre-defined semantic relations between two entities or entity mentions from text. Many methods, such as feature-based and kernel-based methods, have been proposed in the literature. Among them, feature-based methods draw much attention from researchers. However, to the best of our knowledge, existing feature-based methods did not explicitly incorporate the position feature and no in-depth analysis was conducted in this regard. In this paper, we define and exploit nine types of position information between two named entity mentions and then use it along with other features in a multi-class classification framework for Chinese relation extraction. Experiments on the ACE 2005 data set show that the position feature is more effective than the other recognized features like entity type/subtype and character-based N-gram context. Most important, it can be easily captured and does not require as much effort as applying deep natural language processing.".
- 540 hasAuthorList authorList.
- 540 hasTopic Linguistics.
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- 540 keyword "Information Extraction, Information Retrieval".
- 540 keyword "Knowledge representation".
- 540 keyword "Text mining".
- 540 title "Exploiting the Role of Position Feature in Chinese Relation Extraction".
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- 541 creator gaoying-cui.
- 541 creator qin-lu.
- 541 creator wenjie-li.
- 541 creator yirong-chen.
- 541 type InProceedings.
- 541 label "Corpus Exploitation from Wikipedia for Ontology Construction".
- 541 sameAs 541.
- 541 abstract "Ontology construction usually requires a domain-specific corpus for building corresponding concept hierarchy. The domain corpus must have a good coverage of domain knowledge. Wikipedia(Wiki), the world s largest online encyclopaedic knowledge source, is open-content, collaboratively edited, and free of charge. It covers millions of articles and still keeps on expanding continuously. These characteristics make Wiki a good candidate as domain corpus resource in ontology construction. However, the selected article collection must have considerable quality and quantity. In this paper, a novel approach is proposed to identify articles in Wiki as domain-specific corpus by using available classification information in Wiki pages. The main idea is to generate a domain hierarchy from the hyperlinked pages of Wiki. Only articles strongly linked to this hierarchy are selected as the domain corpus. The proposed approach makes use of linked category information in Wiki pages to produce the hierarchy as a directed graph for obtaining a set of pages in the same connected branch. Ranking and filtering are then done on these pages based on the classification tree generated by the traversal algorithm. The experiment and evaluation results show that Wiki is a good resource for acquiring a relative high quality domain-specific corpus for ontology construction.".
- 541 hasAuthorList authorList.
- 541 hasTopic Linguistics.
- 541 isPartOf proceedings.
- 541 keyword "Corpus (creation, annotation, etc.)".
- 541 keyword "Ontologies".
- 541 title "Corpus Exploitation from Wikipedia for Ontology Construction".
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- 542 creator anthony-hartley.
- 542 creator bogdan-babych.
- 542 type InProceedings.
- 542 label "Sensitivity of Automated MT Evaluation Metrics on Higher Quality MT Output: BLEU vs Task-Based Evaluation Methods".
- 542 sameAs 542.
- 542 abstract "We report the results of our experiment on assessing the ability of automated MT evaluation metrics to remain sensitive to variations in MT quality as the average quality of the compared systems goes up. We compare two groups of metrics: those, which measure the proximity of MT output to some reference translation, and those which evaluate the performance of some automated process on degraded MT output. The experiment shows that proximity-based metrics (such as BLEU) loose sensitivity as the scores go up, but performance-based metrics (e.g., Named Entity recognition from MT output) remain sensitive across the scale. We suggest a model for explaining this result, which attributes stable sensitivity of performance-based metrics to measuring cumulative functional effect of different language levels, while proximity-based metrics measure structural matches on a lexical level and therefore miss higher-level errors that are more typical for better MT systems. Development of new automated metrics should take into account possible decline in sensitivity on higher-quality MT, which should be tested as part of meta-evaluation of the metrics.".
- 542 hasAuthorList authorList.
- 542 hasTopic Linguistics.
- 542 isPartOf proceedings.
- 542 keyword "Evaluation methodologies".
- 542 keyword "Machine Translation, SpeechToSpeech Translation".
- 542 title "Sensitivity of Automated MT Evaluation Metrics on Higher Quality MT Output: BLEU vs Task-Based Evaluation Methods".