Text Mining Resources

Some notes on open source text mining resources:

• "The Text Mining Tool Evaluation project will describe the process of text mining, identify non-proprietary software that can search blocks of text to identify reports relevant to the cancer registry, and provide information to state cancer registries regarding different tools available and a comparison of the functionality provided by each tool." Evaluation of Open Source Text Mining Tools for Cancer Surveillance (HTML version from the Google cache)
• "U-Compare is an integrated text mining/natural language processing system based on the UIMA Framework." U-Compare: share and compare tools with UIMA
• "The BioNLP Unstructured Information Management Architecture (UIMA) Component Repository provides UIMA wrappers for novel and well-known 3rd-party NLP tools used in biomedical text prosessing, such as tokenizers, parsers, named entity taggers, and tools for evaluation." BioNLP UIMA Component Respository
• "OpenNLP is an organizational center for open source projects related to natural language processing." OpenNLP
  • OpenNLP projects
  • See also OpenNLP links for other resources.
• FreeLing - written in C++ with features from tokenisation through to part-of-speech tagging, word sense disambiguation
• NLTK - written in Python with a wide range of natural language processing features

Notes from the Text Mining Tutorial at EBI

Links:

• Text Mining in Biomedicine/Exploitation of biomedical semantic resources
  • NaCTeM's Services: KLEIO, FACTA, MEDIE, TerMine, Acromine
  • Overview of resources, biolexicon, bio-ontologies, text mining infrastructure (U-Compare text mining workflows)

Useful resources:

• UK PubMed Central provides annotation of abstracts, covers (or will eventually cover) up to 1.5 million full-text articles
  • Links to the official PubMed results with links back to UK PubMed Central results (presented similarly to official PubMed Central results).
• CiteXplore provides literature search including (but not limited to) PubMed, without domain-specific features
  • Results show PubMed records with search keywords highlighted.
• GoPubMed provides PubMed searching with Gene Ontology categorisation/filtering of search results
  • Results include annotated abstracts which seem keyword-oriented, not gene-oriented, and offer interesting statistics related to publication metadata.
  • Domain-specific annotations can apparently be activated by selecting items from the "what" sidebar, such as protein PDC.
• MedEvi offers sentence-oriented, interaction-oriented querying with wildcards like [disease] supported for an interaction participant
  • It seems debatable whether viewing sentences in isolation is very helpful, especially in the tabular form. I tried searching for phosducin AND "phosducin-like protein" in order to retrieve a document seen in Bioscape (PubMed #12060742), and this query did find it, although PDC AND PDCL (which employs the symbol names) does not, suggesting that there is a textual orientation to the service.
  • Annotated sentences do not appear to be available from this service: links to PubMed are provided.
• EBIMed permits the inspection of results according to the co-occurrence of search terms with other features, thus supporting GoPubMed-style categorisation/filtering as well as gene/protein-related segmentation of results
  • Results are initially presented using a table of "facets" such as co-occurring gene/protein, Gene Ontology categories, drugs and species, with abstracts obtainable upon selection of a particular gene/protein or co-occurring concept.
  • Abstracts are annotated with domain-specific concepts.
• Protein Corral produces results in a way similar to EBIMed but focusing on interaction verbs and confidence measures
  • Results show a selection of "facets" mostly related to interaction context.
  • Abstracts are annotated with domain-specific concepts.
• Whatizit is a service which exposes the EBI text-mining infrastructure
  • Results can mimic other services such as EBIMed (by selecting the whatizitEBIMed pipeline and by issuing A Lucene Query using the input).
  • Abstracts can therefore be annotated with domain-specific concepts if the pipeline supports this (whatizitEBIMed does, whatizitProteinInteraction does not).