Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts

Goertzel, Ben; Pinto, Hugo; Heljakka, Ari; Ross, Michael S.; Pennachin, Cassio; Goertzel, Izabela Freire

doi:10.3115/1567619.1567641

Cited by 12 publications

(7 citation statements)

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“…(This system is also the only one that explicitly targets NP-external arguments of the nominalization.) Goertzel et al (2006) [17] describes a system that contains a “nominalization recognition” component; it is not clear what processing, if any, the recognized nominalizations undergo, and the system is currently in an unevaluated, prototype stage of development. Schuman and Bergler (2006) [18] do not include an interpretive component, but they demonstrate the ability to produce accurate syntactic attachment for post-nominal prepositional phrases using a corpus-based approach, achieving 82% accuracy for this task.…”

Section: Introductionmentioning

confidence: 99%

Nominalization and Alternations in Biomedical Language

2008

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BackgroundThis paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts.Methodology/Principal FindingsWe examined 1,872 tokens of the ten most common domain-specific verbs or their zero-related nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle.Conclusions/SignificanceWe found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedical language. We also report on a previously undescribed alternation involving an adjectival present participle.

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Section: Introductionmentioning

confidence: 99%

Nominalization and Alternations in Biomedical Language

2008

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“…BioLiterate, a system developed by Goertzel et al [ 9 ], is designed to discover relations which are not contained in any individual abstract using probabilistic inference. In contrast to this work their approach is based on a collection of hand-built rules, that map linguistic constructs onto a probabilistic reasoning system.…”

Section: Introductionmentioning

confidence: 99%

Discovering relations between indirectly connected biomedical concepts

2015

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BackgroundThe complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation.ResultsIt is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely “has target”, and “may treat”, are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach.ConclusionsAnalysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0021-5) contains supplementary material, which is available to authorized users.

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“…OpenCog has been used for commercial applications in the area of natural language processing and data mining; e.g. see [GPPG06]. It has also been used to control virtual agents in virtual worlds, at first using an OpenCog variant called the OpenPetBrain [GEA08], and more recently in a more general way using a Minecraft-like virtual environment [GPC + 11].…”

Section: Opencogmentioning

confidence: 99%

Perception Processing for General Intelligence: Bridging the Symbolic/Subsymbolic Gap

Goertzel¹

2012

Artificial General Intelligence

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Bridging the gap between symbolic and subsymbolic representations is a -perhaps the -key obstacle along the path from the present state of AI achievement to human-level artificial general intelligence. One approach to bridging this gap is hybridization -for instance, incorporation of a subsymbolic system and a symbolic system into a integrative cognitive architecture. Here we present a detailed design for an implementation of this approach, via integrating a version of the DeSTIN deep learning system into OpenCog, an integrative cognitive architecture including rich symbolic capabilities. This is a "tight" integration, in which the symbolic and subsymbolic aspects exert detailed real-time influence on each others' operations. Part I of this paper has described in detail the revisions to DeSTIN needed to support this integration, which are mainly along the lines of making it more "representationally transparent," so that its internal states are easier for OpenCog to understand. We describe an extension of the approach beyond vision to include multi-sensory integration, and perception-action integration. We discuss the potential use of this integrated system to control mobile robots, as exemplified via a thought-experiment involving eye-hand coordination.

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Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts

Cited by 12 publications

References 24 publications

Nominalization and Alternations in Biomedical Language

Nominalization and Alternations in Biomedical Language

Discovering relations between indirectly connected biomedical concepts

Perception Processing for General Intelligence: Bridging the Symbolic/Subsymbolic Gap

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