![]() automatically extracted rules based on syntactic structures. manually compiled rules, and Apostolova et al. Rule based methods detect scope by constructing syntactic rules. Generally, hedge scope detection approaches can be divided into two categories: rule based methods and machine learning based methods. This paper focuses on the hedge scope detection task. Hedge scope detection is a difficult task, since it falls within the scope of semantic analysis of sentences exploiting syntactic patterns. However, the results of hedge scope detection are not satisfied. Researches on hedge cue identification have been developed rapidly. The in-sentence scope of the hedge cue “ potentially” is the statement “ the mutagenic DNA deaminases are potentially an important target for hormonal regulation”. The token “ potentially”, namely hedge cue, indicates that the following statement is not backed up by facts. Sentence 1: Our data indicate that the mutagenic DNA deaminases are potentially an important target for hormonal regulation. The shared task contains two subtasks: Task 1 aims to identify hedge cues and Task 2 devotes to detecting the in-sentence scope of a given cue.Ī hedged sentence taken from the CoNLL-2010 Shared Task corpus is shown as follows: The CoNLL-2010 Shared Task is dedicated to the detection of uncertain information. In order to distinguish factual and uncertain information, detecting hedged information is an increasingly important task in biomedical information extraction. ![]() According to the statistics on BioScope corpus, 17.69% of the sentences in abstract section and 22.29% of the sentences in full paper section contain speculative fragments. Hedged information is usually used in science texts, especially in the biomedical domain to express impressions or hypothesized explanations of experimental results. Hedges are linguistic devices that indicate uncertain or unreliable information. Although the candidate boundary selection method is only evaluated on hedge scope detection here, it can be popularized to other kinds of scope learning tasks. Experiments on the CoNLL-2010 Shared Task corpus show that our method achieves 71.92% F1-score on the golden standard cues, which is 4.11% higher than the system without using DCBS. In addition, we employ the composite kernel to integrate lexical and syntactic information and demonstrate the effectiveness of structured syntactic features for hedge scope detection. ![]() This paper proposes a dependency-based candidate boundary selection method (DCBS), which selects the most likely tokens as candidate boundaries and removes the exceptional tokens which have less potential to improve the performance based on dependency tree. The imbalanced instances seriously mislead classifiers and result in lower performance. Previous hedge scope detection methods usually take all tokens in a sentence as candidate boundaries, which inevitably generate a large number of negatives for classifiers. Hedge scope is a sequence of tokens including the hedge cue in a sentence. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguistic scope. Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction.
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