IE for Semi-structured Document: Supervised Approach

Machine-learning based Semi-structured IE

Chia-Hui Chang Department of Computer Science & Information EngineeringNational Central [email protected]

Wrapper Induction

Wrapper An extracting program to extract desired

information from Web pages.Semi-Structure Doc.– wrapper→ Structure Info.

Web wrappers wrap... “Query-able’’ or “Search-able’’ Web sites Web pages with large itemized lists

The primary issues are: How to build the extractor quickly?

Semi-structured IE

Independently of the traditional IEThe necessity of extracting and integrating data from multiple Web-based sources

Machine-Learning Based Approach

A key component of IE systems is a set of extraction patterns that can be generated by machine

learning algorithms.

Related Work

Shopbot Doorenbos, Etzioni, Weld, AA-97

Ariadne Ashish, Knoblock, Coopis-97

WIEN Kushmerick, Weld, Doorenbos, IJCAI-97

SoftMealy wrapper representation Hsu, IJCAI-99

STALKER Muslea, Minton, Knoblock, AA-99 A hierarchical FST

WIEN

N. Kushmerick, D. S. Weld, R. Doorenbos, University of Washington, 1997http://www.cs.ucd.ie/staff/nick/

Example 1

Extractor for Example 1

HLRT

Wrapper Induction

Induction: The task of generalizing from labeled

examples to a hypothesis

Instances: pagesLabels: {(Congo, 242), (Egypt, 20), (Belize, 501), (Spain, 34)}Hypotheses: E.g. (, <HR>, , , ,

)

BuildHLRT

Other Family

OCLR (Open-Close-Left-Right) Use Open and Close as delimiters for eac

h tupleHOCLRT Combine OCLR with Head and Tail

N-LR and N-HLRT Nested LR Nested HLRT

Terminology

Oracles Page Oracle Label Oracle

PAC analysis is to determine how many examples are

necessary to build an wrapper with two parameters: accuracy and confidence :

Pr[E(w)<]>1-, or Pr[E(w)>]<

Probably Approximate Correct (PAC) Analysis

With =0.1, =0.1, K=4, an average of 5 tuples/page, Build HLRT must examine at least 72 examples

1))1(21())1(21( 2||2

22

KT

Empirical Evaluation

Extract 48% web pages successfully. Weakness:

Missing attributes, attributes not in order, tabular data, etc.

SoftmealyChun-Nan Hsu, Ming-Tzung Dung, 1998Arizona State Universityhttp://kaukoai.iis.sinica.edu.tw/~chunnan/mypublications.html

Softmealy Architecture

Finite-State Transducers for Semi-Structured Text Mining Labeling: use a interface to label ex

ample by manually. Learner: FST (Finite-State Transducer) Extractor: Demonstration

http://kaukoai.iis.sinica.edu.tw/video.html

Softmealy Wrapper

SoftMealy wrapper representation Uses finite-state transducer where each d

istinct attribute permutations can be encoded as a successful path

Replaces delimiters with contextual rules that describes the context delimiting two adjacent attributes

Example

4 種情形

Label the Answer Key

Finite State Transducer

b

M -A A

-N

N-UU

e

extract

extractextract

extractskip

skipskip

skip

skip多解決了(N, M) 、(N, A, M)2 個情形

Find the starting position -- Single Pass

新增的定義

Contextual based Rule Learning

TokensSeparators SL ::= … Punc(,) Spc(1) Html() SR ::= C1Alph(Professor) Spc(1) OAlph(of) …

Rule generalization Taxonomy Tree

Tokens

All uppercase string: CALph An uppercase letter, followed by at least

one lowercase letter, C1Alph A lowercase letter, followed by zero or m

ore characters: OAlph HTML tag: HTML Punctuation symbol: Punc Control characters: NL(1), Tab(4), Spc(3)

Rule Generalization

Learning Algorithm

Generalize each column by replacing each token with their least common ancestor

Taxonomy Tree

Generating to Extract the Body

The contextual rules for the head and tail separators are:hL::=C1alpha(Staff) Html(</H2>) NL(1)Html(<HR>) NL(1) Html(<UL>)tR::=Html(</UL>) NL(1) Html(<HR>) NL(1) Html(<ADDRESS>) NL(1) Html() Clalpha(Please)

More Expressive Power

Softmealy allows Disjunction Multiple attribute orders within tuples Missing attributes Features of candidate strings

Stalker

I. Muslea, S. Minton, C. Knoblock, University of Southern California

http://www.isi.edu/~muslea/

STALKER

Embedded Catalog Tree Leaves (primitive items): 所要擷取的東西。 Internal nodes (items):

Homogeneous list, or Heterogeneous tuple.

EC Tree of a page

Extracting Data from a Document

For each node in the EC Tree, the wrapper needs a rule that extracts that particular node from its parentAdditionally, for each list node, the wrapper requires a list iteration rule that decomposes the list into individual tuples.Advantages:

The hierarchical extraction based on the EC tree allows us to wrap information sources that have arbitrary many levels of embedded data.

Second, as each node is extracted independently of its siblings, our approach does not rely on there being a fixed ordering of the items, and we can easily handle extraction tasks from documents that may have missing items or items that appear in various orders.

Extraction Rules as Finite Automata

• Landmarks• A sequence of tokens and wildcards

• Landmark automata• A non-deterministic finite automata

Landmark Automata

• A linear LA has one accepting state

• from each non-accepting state, there are exactly two possible transitions: a loop to itself, and a transition to the next state;

• each non-looping transition is labeled by a landmarks;

• all looping transitions have the meaning “consume all tokens until you encounter the landmark that leads to the next state”.

Rule Generating

1st : terminals: {; reservation _Symbol_ _Word_} Candidate:{; _Symbol_ _HtmlTag_} perfect Disj:{ _HtmlTag_} positive example: D3, D42nd: uncover{D1, D2} Candicate:{; _Symbol_}

Extract Credit info.

Possible Rules

The STALKER Algorithm

Features

Process is performed in a hierarchical manner.沒有 Attributes not in order 的問題。Use disjunctive rule 可以解決 Missing attributes 的問題。

Multi-pass SoftmealyChun-Nan Hsu and Chian-Chi ChangInstitute of Information ScienceAcademia SinicaTaipei, Taiwan

Multi-pass

Tabular style document

(Quote Server)

Tagged-list style document

(Internet Address Finder)

Layout styles and learnability

Tabular style missing attributes, ordering as hints

Tagged-list style variant ordering, tags as hints

Prediction single-pass for tabular style multi-pass for tagged-list style

Tabular result (Quote Server)

Tagged-list result (Internet Address Finder)

Comparison

Both : can handle irregular missing attributes. 對於未見過的 attribute ，需要 training

Single-pass : 允許的 attribute permutations 有限 Single-pass is good for tabular pages 比較快

Multi-pass: Attribute permutations 沒有影響 Multi-pass is good for tagged-list pages 比較慢

Comparison

Quote Server Stalker: 10 example tuples, 79%, 500 test WIEN: the collection beyond learn’s capablity SoftMealy: multi-pass 85%, single-pass 97%

Internet Address Finder Stalker: 80% ~ 100%, 500 test WIEN: the collection beyond learn’s capablity SoftMealy: multi-pass 68%, single-pass 41%,

Comparison

Okra(tabular pages) Stalker: 97%, 1 example tuple WIEN: 100% , 13 example tuples, 30 test SoftMealy: single-pass 100%, 1 example tuple, 30

testBig-book(tagged-list pages) Stalker: 97%, 8 example tuples WIEN: perfect, 18 example tuples, 30 test SoftMealy: single-pass 97%, 4 examples, 30 test multi-pass 100%, 6 examples, 30 test

References

Kushmerick, N. (2000) Wrapper induction: Efficiency and expressiveness. Artificial Intelligence J. 118(1-2):15-68 (special issue on Intelligent Internet Systems). Chun-Nan Hsu and Ming-Tzung Dung. Generating finite-state transducers for semistructured data extraction from the web. Information Systems, 23(8):521-538, Special Issue on Semistructured Data, 1998. Ion Muslea, Steve Minton, Craig Knoblock.Hierarchical Wrapper Induction for Semistructured Information Sources, Journal of Autonomous Agents and Multi-Agent Systems, 4:93-114, 2001 .

Documents

IE for Semi-structured Document: Supervised Approach