Mining External Resourcesfor Biomedical IE

Why, How, What

Malvina Nissimmnissim@inf.ed.ac.uk

Why• goal: Named Entity Recognition

• method: supervised learning

• feature extraction• (text) internal features: word shape, n-grams, ...

protein-indicative features:- of shape a0a0a0a…- followed by /bind/- shorter than 5 characters

• generalisations on training data might be incomplete

• acquired evidence might be absent in test instance

Getting Additional Evidence

internal features might be insufficient, but good evidence might be somewhere else...

Note: some systems (MaxEnt for instance) can easily and successfully integrate a huge number of features

• small and accurate lists of proteins (gazetteers)• use as rules• use as features

• other texts might contain indicative n-grams• other texts might contain indicative n-grams• how to use other texts• which texts to use

How

patterns

“X gene/protein/DNA”

“X sequence/motif”

A. Create patterns (aim, method, input)

B. Search corpus for patterns and obtain counts

C. Use counts as appropriate

1. AIM (granularity)

Create Patterns (I)

distinguish entities from non-entities

distinguish between entities

“X gene OR DNA OR protein”

“X DNA”“X gene”

+ bypass ambiguities and data sparseness– less information

+ more information– ambiguities, data sparseness

“X binds”

1. AIM2. METHOD3. INPUT

Create Patterns (II)

2. METHOD

by hand (experts)

+ high precision, exact target– time consuming, experts needed

automatically (collocations, clustering)

+ no human intervention– lower precision, not necessarily interesting

patterns

1. AIM2. METHOD3. INPUT

3. INPUT (“X gene”)

Create Patterns (III)1. AIM2. METHOD3. INPUT

low frequency words (as estimated from a non-specific corpus)

first output of classifier

NP chunks

words not found in standard dictionary

increase precision but lower recall

prec rec f-scoreall features .813 .861 .836– web .807 .864 .835

What? Google vs PubMed

• PubMed: searchable collection of over 12M biomedical abstracts, more sophisticated search options

• Everything: Google searches over 8 billion pages, raw search, API

“p53 gene”

5,843 documents ~165,000 pages

PubMed Google

Google + PubMed“anything you want” site:<specific_site>

“p53 gene” site:www.ncbi.nlm.nih.gov

Rob Futrelle has this function available on this webpage:

http://www.ccs.neu.edu/home/futrelle/bionlp/search.html

• comment: sometimes PubMed reports “Quoted phrase not found” even when Google finds the phrase.

PubMed provides phrase search only on pre-indexed phrases

PubMed > Google• query expansion

PubMed uses the MeSH headings to match synonyms(it will expand “Pol II” to search for “DNA Polymerase II”)

Google will only try correct misspelling

• field specific search

PubMed allows field-specific searches (eg year)

Google cannot refine its search in this respect

• timeliness

PubMed is updated daily

Google is slow in updating

PubMed > Google (cont’d)

• ranking

Google does a ‘vote’-based ranking: not necessarily good

PubMed does not do any ranking (possibly bad too...)

• truncation and flexibility

PubMed accepts truncated entries and will look for all possible Variations. It will try break phrases if no matches are found.

Google has a rigid search

• manual indexing

PubMed’s MeSH contain keywords not necessarily containedin the abstract

Google cannot find something that is not mentioned in the abstract

• as a rule

• as a feature

+ less false positives+ some systems (MaxEnt) can integrate huge number of features – might still not get used or provide enough evidence

+ sure identification of entities– too powerful -> high risk of false positives

might be OK to use Google: more info but not necessarily precise

might be better to use PubMed: less info but precise

What to Use?(or How to Use the Evidence)

What to Use?(or How to Use the Evidence)

iHOP (Information Hyperlinked Over Proteins) A gene network for navigating the literature

http://www.pdg.cnb.uam.es/UniPub/iHOP

• uses genes and proteins as hyperlinks between sentences and abstracts http://www.pdg.cnb.uam.es/UniPub/iHOP

• each step through the network produces information about one single gene and its interactions

• information retrieved by connecting similar concepts

• precision of gene name and synonym identification: 87-99%

• readers can still check correctness of sentences when they are presented to them

• shortest path between any 2 genes is on average 4 steps only

Nature Genetics, Vol. 36(7), July 2004