=Paper=
{{Paper
|id=None
|storemode=property
|title=Bluima: a UIMA-based NLP Toolkit for Neuroscience
|pdfUrl=https://ceur-ws.org/Vol-1038/paper_7.pdf
|volume=Vol-1038
|dblpUrl=https://dblp.org/rec/conf/gldv/RichardetCT13
}}
==Bluima: a UIMA-based NLP Toolkit for Neuroscience==
https://ceur-ws.org/Vol-1038/paper_7.pdf
Bluima: a UIMA-based NLP Toolkit for
Neuroscience
Renaud Richardet, Jean-Cédric Chappelier, Martin Telefont
Blue Brain Project, EPFL, 1015, Lausanne, Switzerland
renaud.richardet@epfl.ch
Abstract. This paper describes Bluima, a natural language process-
ing (NLP) pipeline focusing on the extraction of neuroscientific content
and based on the UIMA framework. Bluima builds upon models from
biomedical NLP (BioNLP) like specialized tokenizers and lemmatizers.
It adds further models and tools specific to neuroscience (e.g. named
entity recognizer for neuron or brain region mentions) and provides col-
lection readers for neuroscientific corpora.
Two novel UIMA components are proposed: the first allows configuring
and instantiating UIMA pipelines using a simple scripting language, en-
abling non-UIMA experts to design and run UIMA pipelines. The second
component is a common analysis structure (CAS) store based on Mon-
goDB, to perform incremental annotation of large document corpora.
Keywords: UIMA, natural language processing, NLP, neuroinformat-
ics, NoSQL
1 Introduction
Bluima started as an effort to develop a high performance natural language
processing (NLP) toolkit for neuroscience. The goal was to extract structured
knowledge from biomedical literature (PubMed1 ), in order to help neuroscientists
gather data to specify parameters for their models. In particular, focus was set
on extracting entities that are specific to neuroscience (like brain regions and
neurons) and that are not yet covered by existing text processing systems.
After careful evaluation of different NLP frameworks, the UIMA software
system was selected for its open standards, its performance and stability, and
its usage in several other biomedical NLP (bioNLP) projects; e.g. JulieLab [11],
ClearTK [22], DKPRo [6], cTAKES [28], ccp-nlp, U-Compare [15], SciKnowMine
[26], Argo [25]. Initial development went fast and several existing bioNLP models
and UIMA components could rapidly be reused or integrated into UIMA without
the need to modify its core system, as presented in Section 2.1.
Once the initial components were in place, an experimentation phase started
where different pipelines were created, each with different components and pa-
rameters. Pipeline definition in verbose XML was greatly improved by the use
1
http://www.ncbi.nlm.nih.gov/pubmed
of UIMAFit [21] (to define pipelines in compact Java code) but ended up be-
ing problematic, as it requires some Java knowledge and recompilation for each
component or parameter change. To allow for a more agile prototyping, espe-
cially by non-specialist end users, a pipeline scripting language was created. It
is described in Section 2.2.
Another concern was incremental annotation of large document corpus. For
example, when running an initial pre-processing pipeline on several millions of
documents, and then annotating them again at a later time. The initial strategy
was to store the documents on disk, and overwrite them every time they would
be incrementally annotated. Eventually, a CAS store module was developed to
provide a stable and scalable strategy for incremental annotation, as described
in Section 2.3. Finally, Section 3 presents two case studies illustrating the script-
ing language and evaluating the performance of the CAS store against existing
serialization formats.
2 Bluima Components
Bluima contains several UIMA modules to read neuroscientific corpora, perform
preprocessing, create simple configuration files to run pipelines, and persist doc-
uments on the disk.
2.1 UIMA Modules
Bluima’s typesystem builds upon the typesystem from JulieLab [10], which was
chosen for its strong biomedical orientation and its clean architecture. Bluima’s
typesystem adds neuroscientific annotations, like CellType, BrainRegion, etc.
Bluima includes several collection readers for selected neuroscience cor-
pora, like PubMed XML dumps, PubMed Central NXML files, the BioNLP
2011 GENIA Event Extraction corpus [24], the Biocreative2 annotated corpus
[16], the GENIA annotated corpus [14], and the WhiteText brain regions corpus
[8]. A PDF reader was developed to provide robust and precise text extrac-
tion from scientific articles in PDF format. The PDF reader performs content
correction and cleanup, like dehyphenation, removal of ligatures, glyph mapping
correction, table detection, and removal of non-informative footers and headers.
For pre-processing, the OpenNLP-wrappers developed by JulieLab for sen-
tence segmentation, word tokenization and part-of-speech tagging [31] were used
and updated to UIMAFit. Lemmatization is performed by the domain-specific
tool BioLemmatizer [19].Abbreviation recognition (the task of identifying abbre-
viations in text) is performed by BIOADI, a supervised machine learning model
trained on the BIOADI corpus [17].
Bluima uses UIMA’s ConceptMapper [29] to build lexical-based NERs
based on several neuroscientific lexica and ontologies (Table 1). These lexica
and ontologies were either developed in-house or were imported from existing
sources. Bluima wraps several machine learning-based NERs, like OSCAR4
[13] (chemicals, reactions), Linnaeus [9] (species), BANNER [18] (genes and pro-
teins), and Gimli [5] (proteins).
Name Source Scope # forms
Age BlueBrain age of organism, developmental stage 138
Sex BlueBrain sex (male, female) and variants 10
Method BlueBrain experimental methods in neuroscience 43
Organism BlueBrain organisms used in neuroscience 121
Cell BlueBrain cell, sub-cell and region 862
Ion channel Channelpedia [27] ion channels 868
Uniprot Uniprot [1] genes and proteins 143,757
Biolexicon Biolexicon [30] unified lexicon of biomedical terms 2.2 Mio
Verbs Biolexicon verbs extracted from the Biolexicon 5,038
Cell ontology OBO [2] cell types (prokaryotic to mammalian) 3,564
Disease ont. OBO [23] human disease ontology 24,613
Protein ont. OBO [20] protein-related entities 29,198
Brain region Neuronames [3] hierarchy of brain regions 8,211
Wordnet Wordnet [7] general English 155,287
NIFSTD NIF [12,4] neuroscience ontology 16,896
Table 1. Lexica and ontologies used for lexical matching.
2.2 Pipeline Scripting Language
Tool Advantages Disadvantages
UIMA GUI GUI minimalistic UI, can not reuse pipelines
XML descriptor typed (schema) very verbose
raw UIMA java API typed verbose, requires writing and compiling Java
UIMAFit compact, typed requires writing and compiling Java code
Table 2. Different approaches to writing and running UIMA pipelines.
There are several approaches2 to write and run UIMA pipelines (see Table 2).
All Bluima components were initially written in Java with the UIMAFit library,
that allows for compact code. To improve the design and experimentation with
UIMA pipelines, and enable researchers without Java or UIMA knowledge to
easily design and run such pipelines, a minimalistic scripting (domain-specific)
language was developed, allowing UIMA pipelines to be configured with text files,
in a human-readable format (Table 3). A pipeline script begins with the definition
of a collection reader (starting with cr:), followed by several annotation engines
(starting with ae:)3 . Parameter specification starts with a space, followed by the
2
Other interesting solutions exist (e.g. IBM LanguageWare, Argo), but are not open
source.
3
If not package namespace is specified, Bluima loads Readers and Annotator classes
from the default namespace.
parameter name, a column and its value. The scripting language also supports
embedding of inline Python and Java code, reuse of a portion of a pipeline with
include statements, and variable substitution similar to shell scripts. Extensive
documentation (in particular snippets of scripts) is automatically generated for
all components, using the JavaDoc and the UIMAFit annotations.
2.3 CAS Store
A CAS store was developed to persist annotated documents, resume their pro-
cessing and add new annotations to them. This CAS store was motivated by the
common use case of repetitively and incrementally processing the same docu-
ments with different UIMA pipelines, where some pipeline steps are duplicated
among the runs. For example, when performing resource-intensive operations
(like extracting the text from full-text PDF articles, or performing syntactic pars-
ing), one might want to perform these preliminary operation once, store these
results, and subsequently perform different experiments with different UIMA
modules and parameters. The CAS store thus allows to perform the preprocess-
ing only once, to then persist the annotated documents, and to perform the
various experiments in parallel.
MongoDB4 was selected as the datastore backend. MongoDB is a scalable,
high-performance, open-source, schema-free (NoSQL), document-oriented data-
base. No schema is required on the database side, since the UIMA typesystem
acts as a schema, and data is validated on-the-fly by the module. Every CAS is
stored as a MongoDB document, along with its annotations. UIMA annotations
and their features are explicitly mapped to MongoDB fields, using a simple and
declarative language. For example, a Protein annotation is mapped to a prot
field in MongoDB. The mappings are used when persisting and loading from
the database. As of this writing, annotations are declared in Java source files.
In future versions, we plan to store mappings directly in MongoDB to improve
flexibility. Persistence of complex typesystem has not been implemented yet, but
could be easily added in the future.
Currently, the following UIMA components are available for the CAS store:
– MongoCollectionReader reads CAS from a MongoDB collection. Optionally,
a (filter) query can be specified;
– RegexMongoCollectionReader is similar to MongoCollectionReader but al-
lows specifying a query with a regular expression on a specific field;
– MongoWriter persists new UIMA CASes into MongoDB documents;
– MongoUpdateWriter persists new annotations into an existing document;
– MongoCollectionRemover removes selected annotations in a MongoDB col-
lection.
With the above components, it is possible within a single pipeline to read an
existing collection of annotated documents, perform some further processing, add
more annotations, and store theses annotations back into the same MongoDB
documents.
4
http://www.mongodb.org/
3 Case Studies and Evaluation
A first experiment to illustrate the scripting language was conducted on a large
dataset of full-text biomedical articles. A second simulated experiment evalu-
ates the performance of the MongoDB CAS store against existing serialization
formats.
3.1 Scripting and Scale-Out
# collection reader configured with a list of files (provided as external params)
cr: FromFilelistReader
inputFile: $1
# processes the content of the PDFs
ae: ch.epfl.bbp.uima.pdf.cr.PdfCollectionAnnotator
# tokenization and lematization
ae: SentenceAnnotator
modelFile: $ROOT/modules/julielab_opennlp/models/sentence/PennBio.bin.gz
ae: TokenAnnotator
modelFile: $ROOT/modules/julielab_opennlp/models/token/Genia.bin.gz
ae: BlueBioLemmatizer
# lexical NERs, instantiated with some helper java code
ae_java: ch.epfl.bbp.uima.LexicaHelper.getConceptMapper("/bbp_onto/brainregion")
ae_java: ch.epfl.bbp.uima.LexicaHelper.getConceptMapper("/bams/bams")
# removes duplicate annotations and extracts collocated brainregion annotations
ae: DeduplicatorAnnotator
annotationClass: ch.epfl.bbp.uima.types.BrainRegionDictTerm
ae: ExtractBrainregionsCoocurrences
outputDirectory: $2
Table 3. Pipeline script for the extraction of brain regions mention co-occurrences
from PDF documents.
Bluima was used to extract brain region mention co-occurrences from scien-
tific articles in PDF. The pipeline script (Table 3) was created and tested on
a development laptop. Scale-out was performed on a 12-node (144-core) clus-
ter managed by SLURM (Simple Linux Utility for Resource Management). The
383,795 PDFs were partitioned in 767 jobs. Each job was instantiated with the
same pipeline script, using different input and output parameters. The process-
ing completed in 809 minutes (' 8 PDF/s).
3.2 MongoDB CAS Store
The MongoDB CAS store (MCS) has been evaluated against 3 other available
serialization formats (XCAS, XMI and ZIPXMI). For each, 3 settings were eval-
uated: writes (CASes are persisted to disk), reads (CASes are loaded from their
persisted states), and incremental (CASes are first read from their persisted
Write [s] Write Size [MB]
XCAS 4014 41718
XMI 4479 32236
ZIPXMI 5033 4677
MongoDB 3281 16724
Read [s] Incremental [s]
XCAS 3407 31.7
XMI 3090 42.2
ZIPXMI 2790 43.6
MongoDB 730 22.5
Fig. 1. Performance evaluation of MongoDB CAS Store against 3 other serialization
formats.
states, then further processed, and finally persisted again to disk). Writes and
reads were performed on a random sample of 500,000 PubMed abstracts and an-
notated with all available Bluima NERs. Incremental annotation was performed
on a random sample of 5,000 PubMed abstracts and incrementally annotated
with the Stopwords annotator. Processing time and disk space was measured on
a commodity laptop (4 cores, 8GB RAM).
In terms of speed, the MCS significantly outperforms the other formats, espe-
cially for reads (Figure 1). The MCS disk size is significantly smaller than XCAS
and XMI formats, but almost 4 times larger than the compressed ZIPXMI for-
mat. The incremental annotation is significantly faster with MongoDB, and does
not require duplicating or overwriting files, like with the other serialization for-
mats. The MCS could be scaled up in a cluster setup, or using solid states drives
(SSDs). Writes could probably be improved by turning MongoDB’s ”safe mode”
option off. Furthermore, by adding indexes, the MCS can act as a searchable
annotation database.
4 Conclusions and Future Work
In the process of developing Bluima, a toolkit for neuroscientific NLP, we inte-
grated and wrapped several specialized resources to process neuroscientific arti-
cles. We also created two UIMA modules (scripting language and CAS store).
These additions proved to be very effective in practice and allowed us to leverage
UIMA, an enterprise-grade framework, while at the same time allowing an agile
development and deployment of NLP pipelines.
In the future, we will open-source Bluima and add more models for NER and
relationship extraction. We also plan to ease the deployment of Bluima (and its
scripting language) on a Hadoop cluster.
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