Semantic Relation Detection in Bioscience Text Marti Hearst SIMS, UC Berkeley Supported by NSF DBI and a gift from Genentech

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

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) Supported primarily by NSF DBI and a gift from Genentech User Interface / IR Rowena Luk Dr. Emilia Stoica Bioscience Dr. TingTing Zhang Janice Hamer

BioText Architecture Sophisticated Text Analysis Annotations in Database Improved Search Interface

The Nature of Bioscience Text Claim: Bioscience semantics are simultaneously easier and harder than general text. Fewer subtleties Fewer ambiguities “Systematic” meanings Enormous terminology Complex sentence structure easierharder

Entity-Entity Relation Recognition

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

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

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

Protein-Protein interactions Models Dynamic graphical model Naïve Bayes

Graphical Models

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”, “inhibit”…etc. If keyword presents: assign corresponding interaction

Results Accuracies on interaction classification ModelAllPapersCitations Markov Model Naïve Bayes Baselines Most freq. inter TriggerW TriggerW + BO (Roles hidden)

Results: confusion matrix For All. Overall accuracy: 60.5%

Hiding the protein names Replaced protein names with tokens PROT_NAME Selective CXCR4 antagonism by Tat Selective PROT_NAME antagonism by PROT_NAME

Results with no protein names ModelPapersCitations Markov Model44.4 (-23.1%) 52.3 (-2.0%) Naïve Bayes46.7 (-19.2%) 53.4 (-4.1 %)

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

Protein extraction: results RecallPrecisionF-measure All Papers Citations No dictionary used

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

Acquiring Labeled Data using Citances

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.

Acquiring Labeled Data using Citances

A discovery is made … A paper is written …

That paper is cited … and cited … … as the evidence for some fact(s) F.

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!

Citances Nearly every statement in a bioscience journal article is backed up with a cite. It is quite common for papers to be cited 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?

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.

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.

Early results: Paraphrase Creation from Citances

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.

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.

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.

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 The Descent of Hierarchy, and Selection in Relational Semantics, Barbara Rosario, Marti Hearst, and Charles Fillmore, in ACL 2002.

Thank you! Marti Hearst SIMS, UC Berkeley