An environment for relation mining over richly annotated corpora: the case of GENIA

BACKGROUND: The biomedical domain is witnessing a rapid growth of the amount of published scientific results, which makes it increasingly difficult to filter the core information. There is a real need for support tools that 'digest' the published results and extract the most important information. RESULTS: We describe and evaluate an environment supporting the extraction of domain-specific relations, such as protein-protein interactions, from a richly-annotated corpus. We use full, deep-linguistic parsing and manually created, versatile patterns, expressing a large set of syntactic alternations, plus semantic ontology information. CONCLUSION: The experiments show that our approach described is capable of delivering high-precision results, while maintaining sufficient levels of recall. The high level of abstraction of the rules used by the system, which are considerably more powerful and versatile than finite-state approaches, allows speedy interactive development and validation

The gene normalization task in BioCreative III

BACKGROUND: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). RESULTS: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. CONCLUSIONS: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance

Lean semantic interpretation

We introduce two abstraction mechanisms for streamlining the process of semantic interpretation. Configurational descriptions of dependency graphs increase the linguistic generality of interpretation schemata, while interfacing them to lexical and conceptual inheritance hierarchies reduces the amount and complexity of semantic specifications.

Lean Semantic Interpretation

We introduce two abstraction mechanisms for streamlining the process of semantic interpretation. Configurationa

An Integrated Model of Semantic and Conceptual Interpretation from Dependency Structures

Vc t)rot)osc a two-laye, red model for computing mml,ic and conccI)tual intcrprct,ations from dcl)en- (tency sl,ru(:l,m'cs. Abstra(:t, inl, erl)retation s(:hema. ta genttale scman[ic inl;erprcations of 'lninimal' dependency sul)gral)hs, while production rules whose Sl)ccificatkm is rooted in ontok)gical ea[cgories (lcrive a (:moni(:al con(:el)tual interprdaLion fi'om semamic inLcrt)retal,ion struci, ures. Configm'aJonal descrii)fions of del)endcncy graphs increase i;he linguistic generality of interprel,at&)n s(:hemal, a, while inl,erfacing s(:hcmata mM t)rodur:i;ions () lexical and con(:eptual (:lass hierarchies re(hl(:cs l.he alllOllllL an(t coml)lcxil,y of semanl,ie Sl)cciti(:ai,kms