BioLINK Talks

BioLINK,Detroit, June 24 (Edinburgh July 11)

Linking Literature, Information and Knowledge for Biology

• Corpora and Corpus design (2)

• NER and Term Normalisation (3)

• Annotation and Zoning (2)

• Relation Extraction (2)

• Other

Corpus Design for Biomedical Natural Language

ProcessingK. Bretonnel Cohen et al (U of Colorado)

Main Question: why are some (bio-)corpora more used than others? What makes them attractive?

Crucial points:

Take home message: if you want people to use your corpus, use XML, publish annotation guidelines, publicise corpus with dedicated papers, use it for competitions

• format: XML• code several layers of information• publicity: write specific papers about corpus, publicise its availability

Corpora and corpus design

MedTag: a collection of biomedical annotations

L. Smith et al. (National Center for Biotechnology Information, Bethesda, Maryland)

Main Point: MedTag is a database that combines three corpora:

Take home message: integrated data, more accessible, you should try it.

Corpora and corpus design

• MedPost (modified to include 1000 extra sentences)• ABGene• GENETAG (modified to reflect new defs of genes and prots)

The data is available in flat files + software to facilitate loading data into SQL database

• MedPost

• 6700 sentences• annotated for POS and gerund arguments• POS tagger trained on it (97.4% accuracy)

• GENETAG

• 15000 sentences currently released • tagged for gene/protein identification• used in Biocreative

• ABGene

• over 4000 sentences• annotated for gene/protein names• NER tagger trained on it (lower 70s)

Corpora and corpus design

GOOD BAD

Recommended Uses

• training and evaluating POS taggers• training and evaluating NER taggers• developing and evaluating a chunker (for PubMed phrase indexing)• analysis of grammatical usage in medical text• feature extraction for ML

• entity annotation guidelines

• tokenisation! (white spaces were deleted)

Corpora and corpus design

NER and TN

Weakly Supervised Learning Methods for Improving the Quality

of Gene Name NormalizationBen Wellner (MITRE)

1. presenting method of improving quality of training data from BioCreative task1b. System’s performance on improved data is better than on original data

2. weakly supervised methods can be successfully applied for re-labeling noisy training data

Main points

(next week)

NER and TN

Unsupervised gene/protein normalization using automatically

extracted dictionaries

A. Cohen (Oregon Health & Science U., Portland, Oregon)

Main point: dictionary-based gene and protein NER and normalisation system; no supervised training; no human intervention.

• what curated databases are the best collections of names?• are simple rules sufficient for generating ortographic variants?• can common English words be used to decrease false positives?• what is the normalization performance of a dictionary-based approach?

Results: near state-of-the-art; saving on annotation

METHOD

1. Building the dictionary

2. Generating orthographic variants

3. Separating common English words

4. Screening out most common English words

5. Searching the text

6. Disambiguation

Automatically extracted from 5 databases: official symbol,Unique identifiers, name, symbol, synonym, alias fields

Set of 7 simple rules applied iteratively

Dictionary split in two parts: confusion and main dictionary

Note: 5% ambiguous intra-species; 85% across species.Exploit non-ambiguous synonyms; exploit context

NER and TN

NER and TN

A machine learning approach to acronym generation

Tsuruoka et al (Tokyo (Tsujii group), Japan and Salford, UK)

Task: system generates possible acronyms from a given expanded form

Method: ML approach (MaxEnt Markov Model)

Main point: acronym generation as sequence tagging problem

Experiments: - 1901 definition/acronym pairs - several ranked options as output - 75.4% coverage when including top 5 candidates - baseline: take first letters and capitalise them

Classes (tags)

1. SKIP (generator skips the letter)2. UPPER (generator upper-cases letter)3. LOWER (generator lower-cases letter)4. SPACE (generator converts letter into space)5. HYPHEN (generator converts letter into hyphen)

Features

- letter unigram- letter bigram- letter trigam- action history (preceding action)- orthographic (uppercase or not)- length (#words in definition)- letter sequence- distance (between target letter and beginning/tail of word)

NER and TN

Searching for High-Utility Text in the Biomedical Lit.

Shatkay et al. (Queens,Ontario and NYU and NCBI,Maryland)

(Main idea + annotation guidelines)

High Utility Regions = regions in the text that we identify as focusingon scientific findings, stated with a high confidence, and preferablysupported by experimental evidence.

Task: identify text regions that are rich in scientific content, and retrieve docs that have many such regions

Annotation/Zoning

[assertion = sentence or fragment]

Focus = type of information conveyed by assertion- scientific- generic- methodology

Polarity of assertion (positive/negative)

Certainty- complete uncertainty (0)- complete certainty (3)

Evidence = whether assertion is supported by exp evidence- E0 = lack of evidence- E1 = evidence exists but not reported (“it was shown..”)- E2 = evidence not given directly but reference provided- E3 = evidence provided

Direction/Trend = whether assertion reports increase/decrease in specific phenomenon

K=.83

K=.81

K=.70

K=.73

K=.81

Annotation/Zoning

Automatic Highlighting of Bioscience Literature

Annotation/Zoning

H. Wang et al (CS Department, University of Iowa - M. Light group)

Task: automatic highlighting of relevant passages

Approach: IR task- sentence is passage unit- each sentence treated as document- user provides a query

- query box for keywords- example passage highlighting

- system ranks sentences as to relevance to query

(* query expansion system is web-based)

Annotation/Zoning

- Corpus: 13 journal articles each highlighted by a bio graduate student before the request for annotation

- Queries: constructed in retrospect. The annotators created the queries for the articles they had selected. The first highlighted region also used as query

- Processing: tokenisation (LingPipe), indexing (Zettair), ranking of retrieved sentences (Zettair)

- Query Expansion: definitions were used. Google “define” for each word (excluding stopwords).

Over 80% of query words had Google defs.

• poor results • first highlighted passage works better than keywords• Google expansion helps

Using biomedical literature mining to consolidate the set of

known human PPIs

Rel Extr

A. Ramani et al (U of Texas at Austin - Bunescu/Mooney group)

Task: construct a database of known human PPIs by:- combining and linking interactions from existing DBs- mine additional interactions from 750000 Medline abs

Results: - quality of automatically extracted interactions comparable

to that of those extracted manually - overall network of 31609 interactions between 7748 prots

1. Identify proteins in text: CRF tagger

2. Filter out less confident entities

3. Try to detect which pairs of remaining ones are interactions

- use co-citation analysis- train model on existing set

Trained model: a sentence containing 2 protein names is classifiedas correct/wrong. If a sentence has n prots (n ≥ 2), the sentence is replicated n times

- ELCS = Extraction w Longest Common Subsequences (learned rules)- ERK = Extraction using a Relation Kernel

Rel Extr

IntEx: A syntactic role driven PPI extractor for biomedical text

Rel Extr

S. Ahmed et al (Arizona State University)

Task: detect PPIs by reducing complex sentences to simpleclauses and then exploiting syntactic relations

- pronoun resolution (third person and reflexives; simple heuristics)- entity tagging (dictionary lookup + heuristics)- parsing (Link Grammar, dependency based, CMU?)- complex sentence splitting (verb-based approach to extract simple clauses)- interaction extraction (from simple clauses exploiting syntactic roles)