Semi-Supervised Abstraction-Augmented String Kernel for bio-relationship Extraction
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1 Semi-Supervised Abstraction-Augmented String Kernel for bio-relationship Extraction Pavel P. Kuksa, Rutgers University Yanjun Qi, Bing Bai, Ronan Collobert, NEC Labs Jason Weston, Google Research NY Vladimir Pavlovic, Rutgers University Ning Xia, University of Minnesota 1
2 ROADMAP Bio-Relation Extraction Basic String Kernel Abstraction-augmented String Kernel (ASK) Experimental Results Extension to Protein Sequence Classification 2
3 ROADMAP Bio-Relation Extraction Basic String Kernel Abstraction-augmented String Kernel (ASK) Experimental Results Extension to Protein Sequence Classification 3
4 Relation Extraction for Bio-literature MEDLINE: 70 million queries monthly, > 17M articles Impossible to annotated manually Linking biomedical text to databases Bio-Entity Recognition, Bio-Relationship Extraction 4
5 Goal & Challenges Challenges: annotated data is scarce ( MEDLINE millions of unannotated articles (e.g., learn from unlabeled data with limited prior knowledge Bio-Relationships Many important relationships: genetic interaction, disease to phenotype,... case study here: protein-protein interaction (PPI) 5
6 Three Levels: Bio-Relation Extraction (1) Article Level Identify articles about specific relation or not (2) Sentence Level Identify sentences about specific relation or not 6
7 Three Levels: Bio-Relation Extraction (Cont.) (3) Relation Level (pairwise relation) extract pairs of relating entities (e.g. interacting proteins) from a sentence Example: The protein product of c-cbl protooncogene is known to interact with several proteins, including Grb2, Crk, and PI3 kinase, and is though to regulate signaling Interacting pairs: (c-cbl, Grb2), (c-cbl, Crk), etc. 7
8 Essentially: Classification of Word Strings Task Data Labeled Size (1) Article Level BioCreativeII IAS 5495 train abstracts 677 test abstracts (2) Sentence Level AIMED Sentence 1730 sentences (3) Relation Level AIMED Relation 4026 relations (built from (2)) annotated data is scarce 8 extremely large vocabulary (~2 million words in PubMed)
9 ROADMAP Bio-Relation Extraction String Kernel Abstraction-augmented String Kernel (ASK) Experimental Results Extension to Protein Sequence Classification 9
10 String Kernel Use fixed-length feature vectors to represent arbitrary long strings Examples: Word kernel: dot-product of individual word counts Spectrum kernel: dot-product of k-word counts Mismatch kernel: dot-product of k-word counts with inexact matching of m-words Gapped kernel: dot-product of (non-contiguous) k-word counts with g-gaps allowed between words Kuksa, Huang, and Pavlovic, NIPS
11 String Kernels K(X,Y)=<F(X), F(Y)> Spectrum (5) Mismatch(5, 1) string kernel Feature vector F(X) 11
12 e.g. Gapped kernels Count #non-contiguous subsequences of length k and up to g gaps Example Sentence String: SM binds RNA in vitro... Subsequences considered by Gapped Kernel With k =3, g = 1 ( SM [ ] RNA in ), (binds RNA in [ ] ), (binds [ ] in vitro),... Kuksa, Huang, and Pavlovic, NIPS
13 ROADMAP Bio-Relation Extraction Basic String Kernel Abstraction-augmented String Kernel (ASK) Local ASK Global ASK Experimental Results Extension to Protein Sequence Classification 13
14 String Kernels using Unlabeled Data through Word Abstractions String kernel over both words and abstracted word representation learned from unlabeled text K(X,Y)=< (F(X),F (a(x)), (F(Y),F (a(y)) > 14
15 How to Learn Word Abstractions? Word abstractions Grouping Local / Global Step1: train low dimensional embedding for words based on unlabeled data (semantically similar words have close embeddings) Step2: group similar words to generate more abstract entities 15
16 (1) Local ASK: Train word embedding from short text window Positive examples: Text window extracted from unlabeled corpus (PubMed abstracts 1995-present, 1.3G words) Negative examples: Text window with substitution of the middle word by a random word Collobert & Weston, ICML
17 (2) Global ASK: Train word embedding from long text segment Consider word distributions globally (not local window structure) Model relationships between words in long text segments (e.g. article) E(w)... c(w) word dictionary Force g(-) of two documents with similar meanings to have closer representations, with different meanings to be dissimilar Pseudo supervised signals by splitting each Pubmed abstract into two half segments (~4.5M abstracts) 17
18 Example words with same abstractions as sample query words Query Local ASK Global ASK protein medical interact immunopre cipitation ligand, subunit, receptor, molecule surgical, dental, preventive, reconstructive cooperate, compete, interfere, react co-immunoprecipitation, EMSA, autoradiography, RT-PCR proteins, phosphoprotein, isoform, hospital, investigated, research, urology interacting, interacts, associate, member coexpression, two-hybrid, phosphorylated, tbp 18
19 ROADMAP Bio-Relation Extraction Basic String Kernel Abstraction-augmented String Kernel (ASK) Experimental Results Extension to Protein Sequence Classification 19
20 Experiment I : Article Level (relevant article detection) Binary classification: identify abstracts about ( relation protein-protein interaction (not just any Data: BioCreative II competition IAS data Train: 3536 negative, 1959 positive abstracts Test: 338 positive, 339 negative Evaluation: F1 score, ROC 20
21 Experiment I : Article Level (relevant article detection) Method Precision Recall F1 ROC Local ASK Global ASK Mismatch SK (best) BioCreative II Current best system: F (many more handcrafted & syntactic features) 21
22 Experiment II: Sentence Level (relevant sentence detection) Data: AIMed sentences 10-fold cross-validation with 1730 sentences Evaluation: F1 score Method Words Words + Stems Local ASK Global ASK Spectrum SK
23 Experiment III : Relation Level (relation extraction from sentences) Extract all pairwise interacting pairs from a given sentence assuming protein entities have been labeled already Data: AIMed relation dataset 951 positive examples, 3075 negative 10-fold cross-validation Relation data generation: For each regular sentence, with n entities, create C(n,2) copies of the sentence Each copy (example) having only 2 entities replaced with PROT1 and PROT2, all the rest as PROT 23
24 Experiment III : Relation Level (relation extraction from sentences) Method Precision Recall F1 Local ASK Global ASK Baseline (EMNLP07) Baseline system: transductive SVM on features from dependency parsing 24
25 ROADMAP Bio-Relation Extraction Basic String Kernel Abstraction-augmented String Kernel (ASK) Experimental Results Extension to Protein Sequence Classification 25
26 Experiment IV: Structural Classification from Protein Sequences Challenges: Goal: predict structural/ functional class from primary sequence Limited labeled data : highly diverse sequences Millions of unlabeled sequences 26
27 Experiment IV: Local ASK on Protein Sequence Classification Treat each amino acid (AA) as word Train embedding representation for each AA on UniprotKB protein sequence dataset 27
28 Experiment IV: Local ASK on Protein Sequence Classification (cont.) Learned embedding of amino acid (projected 2D with PCA) hydrophilic hydrophobic substitution groups structural properties (hydropathy) 28
29 Experiment IV: Local ASK on Protein Sequence Classification (cont.) mean ROC50 score Method Baseline +Local-ASK Spectrum Mismatch Spatial sample Semi-supervised Cluster kernel Local ASK improves over both supervised and semisupervised baselines 29
30 Related Works Semi-supervised string kernel Word sequence kernel (soft word match, slow ) Cluster kernel just feasible for protein sequences Word abstraction based model Mostly unsupervised, co-occurrence based and no-training e.g. Distributional Similarity (Lee and Pereira, ACL'99) PPI article retrieval: SVM on features o bag-of-words + bag-of-nlps (chunk; phrase; pos; protein mention; non-proteins; title phrase, et al.) PPI relation detection o o Rule/pattern based methods Graph kernels built on complex parsing trees 30
31 Summary Wrapper approach: could apply on both supervised and semi-supervised string kernels Efficient: linear cost in the input length Provided two models for word-feature learning from unsupervised data: local vs. global A unified framework for bre: at multiple levels where few training examples exist Not restricted to biomedical text: workable on general sequence classification tasks Can incorporate other types of word similarities : e.g. LSI 31
32 32 THANKS! Questions?
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