Semi-automatic conversion of BioProp semantic annotation to PASBio annotation

<p>Abstract</p> <p>Background</p> <p>Semantic role labeling (SRL) is an important text analysis technique. In SRL, sentences are represented by one or more predicate-argument structures (PAS). Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). PropBank is the most widely used PAS corpus and annotation format in the newswire domain. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. In this paper, we aim to build a system to convert BIOSMILE's BioProp annotation output to PASBio annotation. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator.</p> <p>Results</p> <p>Our first experiment evaluated our rule-based converter's performance independently from BIOSMILE performance. The converter achieved an F-score of 85.29%. The second experiment evaluated combined system (BIOSMILE + rule-based converter). The system achieved an F-score of 69.08% for PASBio's 29 verbs.</p> <p>Conclusion</p> <p>Our approach allows PAS conversion between BioProp and PASBio annotation using BIOSMILE alongside our newly developed semi-automatic rule generator and rule-based converter. Our system can match the performance of other state-of-the-art domain-specific ML-based SRL systems and can be easily customized for PASBio application development.</p

CHAPTER 6 EVENTS–II: 1 MODELING EVENT RECOGNITION

We report on a research project aimed at the representational and procedural aspects of episodic memory. In this article, we concentrate on the implications of the characteristics of actors in script based episodes on recognition memory, namely their plausibility with respect to the functional roles they are involved in, and the similarity between tested items. The assumptions derived from the theoretical model are contrasted with empirical findings as well as with the results of the cognitive modeling. What comes into play when remembering events? Everyday events are not perceived as unrelated entities, rather they are processed in terms of previous experiences. In case of highly standardized activities, one can reasonably expect generic knowledge structures to develop in order to facilitate processing new experiences as instances of such stereotypical events. In postulating the existence of generic event schemata for well known events, we consider the implications for the encoding, representation, and retrieval of such particular events. A very influential approach to generic event schemata is the script theory by Schank and Abelson (1977). They state, with respect to their famous &amp;quot;restaurant script &amp;quot; example: &amp;quot;[alike episodes] are remembered in terms of