Semantic Relation Detection in Bioscience Text

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Semantic Relation Detectionin Bioscience Text

• Marti Hearst
• SIMS, UC Berkeley
• http//biotext.berkeley.edu
• Supported by NSF DBI-0317510 and a gift from
  Genentech

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BioText Project Goals

• Provide flexible, intelligent access to
  information for use in biosciences applications.
• Focus on
• Textual Information from Journal Articles
• Tightly integrated with other resources
• Ontologies
• Record-based databases

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Project Team

• Project Leaders
• PI Marti Hearst
• Co-PI Adam Arkin
• Computational Linguistics
• Barbara Rosario (graduated)
• Presley Nakov
• Database Research
• Ariel Schwartz
• Gaurav Bhalotia (graduated)

• User Interface / IR
• Rowena Luk
• Dr. Emilia Stoica
• Bioscience
• Dr. TingTing Zhang
• Janice Hamer

Supported primarily by NSF DBI-0317510 and a
gift from Genentech
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BioText Architecture
Sophisticated Text Analysis
Annotations in Database
Improved Search Interface
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The Nature of Bioscience Text

• Claim
• Bioscience semantics are simultaneously easier
  and harder than general text.

easier
harder
Fewer subtleties Fewer ambiguities Systematic
meanings
Enormous terminology Complex sentence structure
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Entity-EntityRelation Recognition
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Two tasks

• Relationship Extraction
• Identify the several semantic relations that can
  occur between two entities (in this case, protein
  names) in bioscience text.
• Entity extraction
• Related problem identify the entities

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The Approach

• Data MEDLINE abstracts and titles
• Graphical models
• Combine in one framework both relation and entity
  extraction
• Both static and dynamic models
• Simple discriminative approach
• Neural network
• Lexical, syntactic and semantic features

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Protein-Protein interactions

• Tasks
• Given sentences from Paper ID, and/or citation
  sentences to ID
• Predict the interaction type given in the HIV
  database for Paper ID
• Extract the proteins involved
• 10-way classification problem

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Protein-Protein interactions

• Models
• Dynamic graphical model
• Naïve Bayes

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Graphical Models
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Evaluation

• Evaluation at document level
• All (sentences from papers citations)
• Papers (only sentences from papers)
• Citations (only citation sentences)
• Trigger word approach
• List of keywords (ex for inhibits inhibitor,
  inhibition, inhibitetc.
• If keyword presents assign corresponding
  interaction

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Results

• Accuracies on interaction classification

Model All Papers Citations
Markov Model 60.5 57.8 53.4
Naïve Bayes 58.1 57.8 55.7
Baselines
Most freq. inter. 21.8 11.1 26.1
TriggerW 20.1 24.4 20.4
TriggerW BO 25.8 40.0 26.1
(Roles hidden)
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Results confusion matrix
For All. Overall accuracy 60.5
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Hiding the protein names

• Replaced protein names with tokens PROT_NAME
• Selective CXCR4 antagonism by Tat
• Selective PROT_NAME antagonism by PROT_NAME

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Results with no protein names
Model Papers Citations
Markov Model 44.4 (-23.1) 52.3 (-2.0)
Naïve Bayes 46.7 (-19.2) 53.4 (-4.1 )
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Protein extraction

• (Protein name tagging, role extraction)
• The identification of all the proteins present in
  the sentence that are involved in the interaction
• These results suggest that Tat - induced
  phosphorylation of serine 5 by CDK9 might be
  important after transcription has reached the 36
  position, at which time CDK7 has been released
  from the complex.
• Tat might regulate the phosphorylation of the RNA
  polymerase II carboxyl - terminal domain in pre -
  initiation complexes by activating CDK7

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Protein extraction results
Recall Precision F-measure
All 0.74 0.85 0.79
Papers 0.56 0.83 0.67
Citations 0.75 0.84 0.79
No dictionary used
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Conclusions of protein-protein interaction project

• Encouraging results for the automatic
  classification of protein-protein interactions
• Use of an existing database for gathering labeled
  data
• Use of citations

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Acquiring Labeled Data using Citances
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BioScience Researchers

• Read A LOT!
• Cite A LOT!
• Curate A LOT!
• Are interested in specific relations, e.g.
• What is the role of this protein in that pathway?
• Show me articles in which a comparison between
  two values is significant.

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Acquiring Labeled Data using Citances
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A discovery is made
A paper is written
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That paper is cited
and cited
and cited
as the evidence for some fact(s) F.
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Each of these in turn are cited for some fact(s)
until it is the case that all important facts
in the field can be found in citation sentences
alone!
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Citances

• Nearly every statement in a bioscience journal
  article is backed up with a cite.
• It is quite common for papers to be cited 30-100
  times.
• The text around the citation tends to state
  biological facts. (Call these citances.)
• Different citances will state the same facts in
  different ways
• so can we use these for creating models of
  language expressing semantic relations?

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Using Citances

• Potential uses of citation sentences (citances)
• creation of training and testing data for
  semantic analysis,
• synonym set creation,
• database curation,
• document summarization,
• and information retrieval generally.
• Some preliminary results
• Citances to a document align well with a
  hand-built curation.
• Citances are good candidates for paraphrase
  creation.

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Issues for Processing Citances

• Text span
• Identification of the appropriate phrase, clause,
  or sentence that constructs a citance.
• Correct mapping of citations when shown as lists
  or groups (e.g., 22-25).
• Grouping citances by topic
• Citances that cite the same document should be
  grouped by the facts they state.
• Normalizing or paraphrasing citances
• For IR, summarization, learning synonyms,
  relation extraction, question answering, and
  machine translation.

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Early resultsParaphrase Creation from Citances
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Sample Sentences

• NGF withdrawal from sympathetic neurons induces
  Bim, which then contributes to death.
• Nerve growth factor withdrawal induces the
  expression of Bim and mediates Bax dependent
  cytochrome c release and apoptosis.
• The proapoptotic Bcl-2 family member Bim is
  strongly induced in sympathetic neurons in
  response to NGF withdrawal.
• In neurons, the BH3 only Bcl2 member, Bim, and
  JNK are both implicated in apoptosis caused by
  nerve growth factor deprivation.

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Their Paraphrases

• NGF withdrawal induces Bim.
• Nerve growth factor withdrawal induces the
  expression of Bim.
• Bim has been shown to be upregulated following
  nerve growth factor withdrawal.
• Bim implicated in apoptosis caused by nerve
  growth factor deprivation.
• They all paraphrase
• Bim is induced after NGF withdrawal.

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Paraphrase Creation Algorithm

• 1. Extract the sentences that cite the target.
• 2. Mark the NEs of interest (genes/proteins, MeSH
  terms)
• and normalize.
• 3. Dependency parse (MiniPar).
• 4. For each parse
• For each pair of NEs of interest
• i. Extract the path between them.
• ii. Create a paraphrase from the path.
• 5. Rank the candidates for a given pair of NEs.
• 6. Select only the ones above a threshold.
• 7. Generalize.

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Relevant Papers

• Citances Citation Sentences for Semantic
  Analysis of Bioscience Text, Preslav Nakov, Ariel
  Schwartz, and Marti Hearst, in the SIGIR'04
  workshop on Search and Discovery in
  Bioinformatics.  
• Classifying Semantic Relations in Bioscience
  Text, Barbara Rosario and Marti Hearst, in ACL
  2004.  
• The Descent of Hierarchy, and Selection in
  Relational Semantics, Barbara Rosario, Marti
  Hearst, and Charles Fillmore, in ACL 2002.

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Thank you!

• Marti Hearst
• SIMS, UC Berkeley
• http//biotext.berkeley.edu