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Slide1: 

Boosting Unsupervised Language Acquisition with ADIOS Eytan Ruppin Schools of Computer Science & Medicine Tel-Aviv University December 2006 http://www.tau.ac.il/~ruppin

Overview: 

Overview An overview of ADIOS The ConText algorithm Monty Python something completely different – Mex in the service of mankind..

Slide3: 

Unsupervised Learning of Natural Languages - ADIOS Zach Solan School of Physics and Astronomy Tel-Aviv University http://www.tau.ac.il/~zsolan Zach Solan, David Horn, Eytan Ruppin Tel Aviv University Shimon Edelman Cornell

Previous work: 

Previous work Probabilistic Context Free Grammars ‘Supervised’ induction methods Little work on raw data Mostly work on artificial CFGs ABL, EMILE, etc. – Unsupervised parsing - do not examine the generative capacity of the grammar

Our goal: 

Our goal Given a corpus of raw text separated into sentences, we want to derive a specification of the underlying grammar This means we want to be able to Create new unseen grammatically correct sentences (Precision) Accept new unseen grammatically correct sentences and reject ungrammatical ones (Recall)

A broad scope..: 

A broad scope.. Natural language sentence • A musical piece • A biological sequence (DNA, proteins, . . .) • Successive actions of a WEB user • Chronological series

ADIOS in outline: 

ADIOS in outline Composed of three main elements A representational data structure A segmentation criterion (MEX) A generalization ability We will consider each of these in turn

Slide8: 

Is that a dog? Is that a cat? Where is the dog? And is that a horse? The Model: Graph representation with words as vertices and sentences as paths.

ADIOS in outline: 

ADIOS in outline Composed of three main elements A representational data structure A segmentation criterion (MEX) A generalization ability

Detecting significant patterns: 

Detecting significant patterns Identifying patterns becomes easier on a graph Sub-paths are automatically aligned

Motif EXtraction: 

Motif EXtraction

Slide12: 

Rewiring the graph Once a pattern is identified as significant, the sub-paths it subsumes are merged into a new vertex and the graph is rewired accordingly. Repeating this process, leads to the formation of complex, hierarchically structured patterns.

ADIOS in outline: 

ADIOS in outline Composed of three main elements A representational data structure A segmentation criterion (MEX) A generalization ability

Generalization – defining an equivalence class: 

Generalization – defining an equivalence class show me flights from philadelphia to san francisco on wednesdays list all flights from boston to san francisco with the maximum number of stops show flights from dallas to san francisco may i see the flights from denver to san francisco please

Generalization: 

Generalization

Context-sensitive generalization: 

Context-sensitive generalization Slide a context window of size L across current search path For each 1≤i≤L look at all paths that are identical with the search path for 1≤k≤L, except for k=i Define an equivalence class containing the nodes at index i for these paths Replace i’th node with equivalence class Find significant patterns using MEX criterion

Bootstrapping: 

Bootstrapping What are the cheapest from to that stop in atlanta boston philadelphia denver Generalized search path II: denver philadelphia dallas flight flights airfare fare

Bootstrapping: 

Bootstrapping

The ADIOS algorithm: 

The ADIOS algorithm Initialization – load all data into a pseudograph Until no more patterns are found For each path P Create equivalence class candidates Detect significant patterns using MEX If found, add best new pattern and its associated equivalence classes and rewire the graph

The ATIS experiments: 

The ATIS experiments ATIS-NL is a 13,043 sentence corpus of natural language Transcribed phone calls to an airline reservation service ADIOS was trained on 12,700 sentences of ATIS-NL The remaining 343 sentences were used to assess recall Precision was determined with the help of 8 graduate students from Cornell University

The ATIS experiments: 

The ATIS experiments ADIOS’ performance scores – Single learner Recall – ~12%, approaching 40% with multiple learners (70% with semantic bootstrapping) Precision – 50% For comparison, Human-crafted ATIS-CFG reached – Recall – 45% Precision - <1%(!)

English as Second Language test: 

English as Second Language test A single instance of ADIOS was trained on the CHILDES corpus 120,000 sentences of transcribed child-directed speech Subjected to the Goteborg multiple choice ESL test 100 sentences, each with open slot Pick correct word out of three ADIOS got 60% of answers correctly An average ninth-grader performance

Language dendogram: 

Language dendogram

Language modeling: Perplexity : 

Language modeling: Perplexity The lower perplexity of ADIOS, compared with results from the standard benchmarks (McCandless & Glass 1993, Chelba 2001, and Kermorvant 2004).

So far so good..: 

So far so good..

Shortcomings of ADIOS: 

Shortcomings of ADIOS Problem I: Equivalence classes may be declared without sufficient evidence: That two words are contained in an identical 5-word window is often enough for them to be declared interchangeable Negatively impacts the learned grammar’s precision Brian likes to go fishing John and Brian like to go fishing

Shortcomings of ADIOS: 

Shortcomings of ADIOS Problem II: Segmentation criterion is decoupled from search for equivalence classes, and from the potential contribution of patterns to generalization. from baltimore from baltimore san francisco to atlanta to

Slide28: 

The ConText Algorithm Ben Sandbank School of Computer Science Tel-Aviv University http://www.cs.tau.ac.il/~sandban Eytan Ruppin Tel Aviv University Shimon Edelman Cornell

The ConText Algorithm: 

The ConText Algorithm Main concepts Maintain the ADIOS principle of context-dependent generalization, but; Utilize distributional clustering concepts to safely detect interchangeability (Problem I) Declare constituents only when necessary for generalization (Problem II)

Algorithm outline: 

Algorithm outline Calculate the context distribution of all word-sequences that occur more than K times the context of a sequence is the l-word neighborhood on both its sides Cluster sequences according to distance in context-space Find optimal alignment within each cluster Yielding interchangeable words and expressions (note that this process obviates ADIOS’ segmentation process) Merge sequences within each cluster to yield a single generalized sequence Load resulting equivalence classes back to the corpus in a context-sensitive way, using the remaining terminal words. The collection of sentences and equivalence classes hierarchy obtained forms a context-free grammar.

Clustering procedure: 

Clustering procedure Each word sequence is represented as a high-dimensional vector constructed in the following way: For each sequence, create a context vector on its left and on its right The i’th entry in the left (right) context vector contains the number of times the corresponding l-word-sequence appeared to the immediate left (right) of the represented sequence The two vectors are linked to form one long vector The distance metric used between two sequences is the angle between their corresponding vectors No smoothing, TF/IDF or the like is performed A parameter determines the minimum distance between two sequences that allows for their clustering in the same cluster

Alignment procedure: 

Alignment procedure Aims at finding interchangeable words and word sequences between every pair of alignable sequnces A straightforward extension of dynamic programming, where in each step we look for the best alignment of the i,j+1 prefixes, given the best alignments up to the i,j prefixes (yielding an O(n^4) complexity). Input – clusters of word sequences Output – A substitution cost matrix between subsequences Which may each contain more than one word Insertions and deletions currently not supported The optimal pairwise alignments within each cluster

Alignment procedure: 

Alignment procedure Initialize substitution cost matrix The cost of substituting subsequence of length i words with subsequence of length j words is min(i, j) Unless they’re identical and the cost is zero Iterate until convergence in alignment Find the optimal pairwise alignment between each pair of sequences contained in the same cluster For each substitution used in the optimal alignment, multiply its cost in the substitution matrix by alpha<1 Allows ‘sharing information’ between clusters

Toy Sample - Resulting Sentence: 

Toy Sample - Resulting Sentence I’d like to order a flight

Results: 

Results Tested on ATIS-2 A natural language corpus of transcribed spontaneous speech Contains 11,670 sentences 11,370 were used for training, 300 for recall testing Two main parameters affect quality of results D - The minimum distance for two sequences to become clustered K - The minimum number of times a sequence must appear in the corpus to be considered for clustering

Results – K=25: 

Results – K=25 ADIOS results: Recall 0.12 Precision 0.5

Results – D=0.7: 

Results – D=0.7 Surprisingly – no clear connection between K and recall/precision rates Although a general precision trend is discernable

Conclusions: 

Conclusions On ATIS-2, ConText is superior in performance to ADIOS Use of distributional clustering method provides significantly higher precision Recall is marginally improved, Additional iterations (after rewiring) are expected to improve recall further, but when naively implemented hamper precision Introducing asynchronous dynamics in the alignment procedure may enable higher recall through multiple learners. Quality of results seems to depend more on D than on K

Functional representation of enzymes by specific peptides: 

Functional representation of enzymes by specific peptides David Horn Tel Aviv University http://horn.tau.ac.il in collaboration with Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Eytan Ruppin

Application to Biology: 

Application to Biology Vertices of the graph: 4 or 20 letters Paths: gene-sequences, protein-sequences. Transition probabilities on the graph are proportional to the number of paths Trial-path: testing transition probabilities to extract motifs

Slide41: 

The functionality of an enzyme is determined according to its EC number Classification Hierarchy [ Webb, 1992 ] n1.n2: sub-class / 2nd level n1.n2.n3: sub-subclass / 3rd level n1.n2.n3.n4: precise enzymatic activity Enzyme Function EC number: n1.n2.n3.n4 (a unique identifier)

Slide42: 

Mex motifs are extracted from the enzymes data A linear SVM is applied to evaluate the predictive power of MEX motifs Enzyme sequences are randomly partitioned into a training-set and a test-set (75%-25%) 16 2nd level classification tasks 32 3rd level classification tasks Performance measurement: Jaccard Score The train-test procedure was repeated 40 times to gather sufficient statistics Methods

Slide43: 

Assessing Classification Results Classifications performance are compared to those of two other methods: Smith-Waterman algorithm [ Identification of Common Molecular Subsequences. JMB, 1981 ] p-value of pairwise similarity score SVMProt [ Cai et al. Nucleic Acids Research, 2003 ] physicochemical properties of AA Both methods require additional information apart from the raw sequential data

Slide44: 

Jaccard Score EC Subclass α < 0.01 2nd Level Classification

Slide45: 

3rd Level Classification α < 0.01 Jaccard Score EC Sub-subclass

EC hierarchy and specific peptides: 

EC hierarchy and specific peptides

questions: 

questions Do SPs have biological relevance? Are they accounted for in the literature? Generalization of the classification The question of bias Remote homology

Examples of two level 4 classes: 

Examples of two level 4 classes 5.1.3.20 protobacteria 5.1.3.2 bacteria and eukaryotes

P67910: 

P67910 1,2,3 are active sites S,Y,K 4: RYFNV

Generalization: double annotation: 

Generalization: double annotation Doubly annotated enzymes were not included in the training set (the data) 260 such enzymes exist. SPs have 421 hits on 69 of them. Most agree with annotations. 30 disagree, leading to new predictions.

Remote homology: 

Remote homology Examples taken from Rost 2002, pointing out disparity between identity and function. Here function verified by SPs.

Summary: 

Summary Specific peptides (average length 8.4), extracted by MEX, are useful for functional representation of most enzymes (average length 380). SPs are deterministic motifs conserved by evolution, hence expected to have biological significance. SP4s are observed to cover active and binding sites. Some other SPs seem to be important, judging by their spatial locations. SPs are important for remote homologies