Specific set of verbs, but it cannot assign a name to
Specific set of verbs, but it cannot assign a name to the extracted relation.Evaluating general relationsTable 3 Performance of our post-processing on proteins and drugs detectionProtein MetaMap After filtering Drug MetaMap After filtering 62.61 93.96 20.86 83.26 79.51 62.47 33.04 71.38 Acc. 58.10 88.93 Pre. 15.72 55.77 Re. 63.21 47.61 F. ( ) 25.18 51.These scores were generated by using the evaluation script of CoNLL 2000.For the purpose of evaluation, we have created our original test set by randomly selecting 500 sentences from MEDLINE. Our system was given this set as input, and returned a set of binary relations as output. A binary relation in our setting is composed by two biomedical entities and it usually represents some association or effect between the entities. We call those binary relations general relations to distinguish them from those of specific types, e.g., PPI or DDI. To evaluate the general relations, we have defined evaluation criteria for entities and relations.Nguyen et al. BMC Bioinformatics (2015) 16:Page 5 ofEvaluating entities:An entity is correct if and only if (1) it is a noun or a base noun phrase (a unit noun phrase that does not include other noun phrases), and (2) its content words represent the complete meaning within the sentence containing it. The first condition is set up in this criterion because MetaMap can only detect entities that are nouns or base noun phrases. The second one is to guarantee the meaning of the annotated entities. For example, Figure 1(a) shows a relation between two entities `Laminin’ and `membrane’. In this case, Anlotinib cost 25962748″ title=View Abstract(s)”>PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/25962748 the entity `Laminin’ is correct, but the entity `membrane’ is not. The reason is that `membrane’ does not reflect the full meaning intended in this sentence; the right entity should be `basal membrane’.Evaluating relations:and (2) extraction of relations predefined in PPI and DDI corpora.Evaluation results on general relationsA correct relation must satisfy the following two conditions: ?The two entities composing the relation must be correct according to the above-mentioned criterion. ?The relationship between two entities in a correct relation must be described explicitly by some linguistic expression. Any relations that break one of the above conditions are considered to be incorrect. For example, the extracted relation in Figure 1(c) is correct since it meets our criteria, while the extracted relations in (a) and (b) are not. The relation in (a) does not meet the first criterion since the entity `membrane’ is not correct. The relation in (b) does not meet the second criterion because this sentence only lists two selected parameters that are related to `Sertoli cells’ and `tubular basal lamina’, and no relationship between these two entities is mentioned. More details about our evaluation guideline can be seen in the Additional file 1.Results and discussionIn this work, we conducted evaluations in two scenarios: (1) extraction of all possible relations in sentences randomly sampled from MEDLINE, in which we attempt to estimate the performance of PASMED from a perspective of open-domain relation extraction from MEDLINE,For comparison, we conducted experiments using two state-of-the-art OIE systems for general domains, namely, ReVerb [11] and OLLIE [12]. We employed these two systems to extract relevant NP pairs in place of our PAS patterns. The other processes were applied in exactly the same way as our system. We also compared our system with the latest version of S.
