Risk management is a vital activity to ensure employee safety in construction projects. Various documents provide important supporting evidence, including details of previous incidents, consequences and mitigation strategies. Potential hazards may depend on a complex set of project-specific attributes, including activities undertaken, location, equipment used, etc. However, finding evidence about previous projects with similar attributes can be problematic, since information about risks and mitigations is usually hidden within and may be dispersed across a range of different free text documents. Automatic named entity recognition (NER), which identifies mentions of concepts in free text documents, is the first stage in structuring knowledge contained within them. While developing NER methods generally relies on annotated corpora, we are not aware of any such corpus targeted at concepts relevant to construction safety. In response, we have designed a novel named entity annotation scheme and associated guidelines for this domain, which covers hazards, consequences, mitigation strategies and project attributes. Four health and safety experts used the guidelines to annotate a total of 600 sentences from accident reports; an average inter-annotator agreement rate of 0.79 F-Score shows that our work constitutes an important first step towards developing tools for detailed semantic analysis of construction safety documents.

Assessing the suitability of an Open Source Software project for adoption requires not only an analysis of aspects related to the code, such as code quality, frequency of updates and new version releases, but also an evaluation of the quality of support offered in related online forums and issue trackers. Understanding the content types of forum messages and issue trackers can provide information about the extent to which requests are being addressed and issues are being resolved, the percentage of issues that are not being fixed, the cases where the user acknowledged that the issue was successfully resolved, etc. These indicators can provide potential adopters of the OSS with estimates about the level of available support. We present a detailed hierarchy of content types of online forum messages and issue tracker comments and a corpus of messages annotated accordingly. We discuss our experiments to classify forum messages and issue tracker comments into content-related classes, i.e.~to assign them to nodes of the hierarchy. The results are very encouraging.

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

Cross-lingual information retrieval (CLIR) involving the Chinese language has been thoroughly studied in the general language domain, but rarely in the biomedical domain, due to the lack of suitable linguistic resources and parsing tools. In this paper, we describe a Chinese-English CLIR system for biomedical literature, which exploits a bilingual ontology, the ``eCMeSH Tree"""". This is an extension of the Chinese Medical Subject Headings (CMeSH) Tree, based on Medical Subject Headings (MeSH). Using the 2006 and 2007 TREC Genomics track data, we have evaluated the performance of the eCMeSH Tree in expanding queries. We have compared our results to those obtained using two other approaches, i.e. pseudo-relevance feedback (PRF) and document translation (DT). Subsequently, we evaluate the performance of different combinations of these three retrieval methods. Our results show that our method of expanding queries using the eCMeSH Tree can outperform the PRF method. Furthermore, combining this method with PRF and DT helps to smooth the differences in query expansion, and consequently results in the best performance amongst all experiments reported. All experiments compare the use of two different retrieval models, i.e. Okapi BM25 and a query likelihood language model. In general, the former performs slightly better.

Due to the rapid growth in the volume of biomedical literature, there is an increasing requirement for high-performance semantic search systems, which allow biologists to perform precise searches for events of interest. Such systems are usually trained on corpora of documents that contain manually annotated events. Until recently, these corpora, and hence the event extraction systems trained on them, focussed almost exclusively on the identification and classification of event arguments, without taking into account how the textual context of the events could affect their interpretation. Previously, we designed an annotation scheme to enrich events with several aspects (or dimensions) of interpretation, which we term meta-knowledge, and applied this scheme to the entire GENIA corpus. In this paper, we report on our experiments to automate the assignment of one of these meta-knowledge dimensions, i.e. Manner, to recognised events. Manner is concerned with the rate, strength intensity or level of the event. We distinguish three different values of manner, i.e., High, Low and Neutral. To our knowledge, our work represents the first attempt to classify the manner of events. Using a combination of lexical, syntactic and semantic features, our system achieves an overall accuracy of 99.4%.

Biomedical corpora annotated with event-level information provide an important resource for the training of domain-specific information extraction (IE) systems. These corpora concentrate primarily on creating classified, structured representations of important facts and findings contained within the text. However, bio-event annotations often do not take into account additional information (meta-knowledge) that is expressed within the textual context of the bio-event, e.g., the pragmatic/rhetorical intent and the level of certainty ascribed to a particular bio-event by the authors. Such additional information is indispensible for correct interpretation of bio-events. Therefore, an IE system that simply presents a list of “bare” bio-events, without information concerning their interpretation, is of little practical use. We have addressed this sparseness of meta-knowledge available in existing bio-event corpora by developing a multi-dimensional annotation scheme tailored to bio-events. The scheme is intended to be general enough to allow integration with different types of bio-event annotation, whilst being detailed enough to capture important subtleties in the nature of the meta-knowledge expressed about different bio-events. To our knowledge, our scheme is unique within the field with regards to the diversity of meta-knowledge aspects annotated for each event.

This paper reports on the design and construction of a bio-event annotated corpus which was developed with a specific view to the acquisition of semantic frames from biomedical corpora. We describe the adopted annotation scheme and the annotation process, which is supported by a dedicated annotation tool. The annotated corpus contains 677 abstracts of biomedical research articles.