Extraction of semantic biomedical relations from text using conditional random fields

Abstract

BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.ResultsWe benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.ConclusionWe extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

“…Examples of supervised methods using kernels to encode lexical and syntactic features include [19,29]. Other supervised approaches model the problem of RE as a sequence labeling problem and apply Markov logic and conditional random fields to identify the relations between two entities [43,12]. The need of hand-labeled training examples of these approaches makes it difficult to scale to heterogeneous and large environments such as the Web.…”

“…Supervised machine learning techniques have also been successfully applied to train relation classifiers on human annotated texts in the biomedical domain [19,12,29]. They view the RE process as a classification problem, where the task consists in finding out if a particular relation holds between two entities in a sentence.…”

“…A concept of the Metathesaurus can be assigned to one or more semantic types in the Semantic Network. The UMLS semantic groups 12 is an even smaller and coarser-grained set of semantic type groupings. It contains 15 groups.…”

Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data

“…Automated text mining of these data has been already applied to derive gene-disease associations (e.g. [12,13]); however, these approaches require recognizing gene names using automated text mining methods that suffer from low accuracy [14,15]. Another way to extract gene-disease associations from the literature is to integrate the numerous manually curated annotations of PubMed citations for genes or diseases.…”

Proceedings of the COLING Workshop on Synchronic and Diachronic Approaches to Analyzing Technical Language

“…are defined relative to a specific field like Chemistry or Biology (Kim et al, 2003;Corbett et al, 2007;Bada et al, 2010). These classes are used for narrow tasks, e.g., Information Extraction (IE) slot filling tasks within a particular genre of interest (Giuliano et al, 2006;Bundschus et al, 2008;BioCreAtIvE, 2006). Other projects are limited to Information Extraction tasks that may not be terminology-specific, but have terms as arguments, e.g., (Schwartz and Hearst, 2003; detect abbreviation and definition relations respectively and the arguments are terms.…”