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Information Retrieval and Information Extraction

Information Retrieval and Information Extraction. image: google. Akhil Deshmukh (06D5007) ‏ Narendra Kumar (06D05008) ‏ Kumar Lav (06D05012) ‏. IR and IE. IR is the process of selecting documents from the collection. IE is retrieval of specific information from the document.

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Information Retrieval and Information Extraction

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  1. Information Retrieval and Information Extraction image: google Akhil Deshmukh (06D5007)‏ Narendra Kumar (06D05008)‏ Kumar Lav (06D05012)‏

  2. IR and IE IR is the process of selecting documents from the collection. IE is retrieval of specific information from the document. IE transform the unstructured data into the structured. They together provide powerful tool for text processing.

  3. contd .... IR and IE can be seen in our day to day life. The email-inbox The searches we make on internet. This seminar itself is a result of IR and IE.

  4. Information retrieval Tasks • Search Engines (eg. Google and Yahoo). • Feedback. • Meta search. • Data clustering and Visualization search results.

  5. Clustering of search results

  6. IR measures • Division of documents. • Precision • Recall • F-score.

  7. Models for IR • Language Models • In LM, the idea is to compute the conditional probability P(q | d), i.e. the probability of generating a query q given the observation of a document d. • Vector Space Model (VSM)‏ • VSM is a algebraic model, for representing objects as vectors of identifiers.

  8. Language Models for IR • Model 1 • Given a query q, we want to compute the relevance of document s. We find the probability - p(r=1| q, d)‏ , where r is the binary label for ‘relevant’. • Applying Bayes rule - p(r = 1| q, d) =p(d, q | r=1) p(r=1)‏ p(q , d) We make two assumptions – 1. p(q | d, r =1) is computed from unigram model created from d, • 2. p(q | d, r = 0) = p(q | r = 0)‏ • Here we consider the ratio of log values – • log p(r = 1 | q, d) =log p(q | d, r=1) +log p( r=1 | d)‏ • p(r = 0 | q, d) p(q | d, r=0) p(r=0 | d)‏ • Disadvantages

  9. Vector Space Model for IR • Each document d is represented as a scalar tf.idf, known as tf-idf scheme. • Where “ tf ” is normalize term frequency, and “ idf “ is the inverse document frequency. • tfw = c(w, d)/ maxv c(v, d)‏ , c =count • idfw = log(N/nw) N = no. Of documents in collection • n = no. Of documents containing w • so we have tf . idfw = tfw × idfw.

  10. Fig: http://www.miislita.com/term-vector/term-vector-3.html

  11. Documents are ranked by their cosine similarity to q • ‏ • sim(d, q) = cos( )‏ • =dT q=∑j Wd,j x Wq,j • |d| |q||d| |q| • here|d|= ∑j Wd,j2, |q| = ∑j Wq,j2 • and Wd,j = weight of term j in document d • i.e. Wd,j = tfd,j x idfj • Wq,j = weight of term j in query q • Normalization ??

  12. Trigger Language Model Here we calculate the probability p(q| Md). where Md is the language model for document d. a) Estimation p(qi| Md) = tf(qi, d) , tf(qi, d)= frequency of qi in d dlddld = total no. of terms in d. b) Zero counts p(qi | Md) = p(qi | Md) , if tf(qi, d) >0 = cfqi , otherwise cs cfqi = raw count of qi in all documents cs = total no. of terms in all documents c) Smoothing: calculate pavg(qi) . d)Final term probability, p#(qi | Md) = p(qi | Md)(1-R) x pavg(qi)R , if tf(qi, d)>0 = cfqi , otherwise cs

  13. To minimize the smoothing risk, R = [1/(1+f)] x [f/(1+f)]tf Where f is the mean term frequency of term, f = pavg(qi) x dld e) Finally: p(q | Md) = πqi∈qp#(qi | Md) x πqi∈q [1- p#(qi | Md)] Fig by :ZHANG Jun-lin, SUN Le QU Wei-min, SUN Yu-fang,2002

  14. Information Extraction (IE)‏ Task of locating specific information from a natural language document. Convert information from different text documents into database entries (structured format). It runs on the top of IR systems.

  15. Is IE and IR same ? IE systems do not understand the text in the input documents. IR retrieves relevant documents, IE extracts relevant information IE sees text as structure, for IR they are bag of words. Both, together provide powerful tools for text processing.

  16. Extraction Free text: Natural language. Articles in newspapers etc. Uses NLP techniques. like POS tagging. Rules based on syntactic and semantic information. Structured Text: Text as in database or tables. Extraction is easy and direct. Rules are based on the structure of the text.

  17. Extractioncontd... Semi-structured: are between free and structured text. comprises of phrases. regular expressions are used. Information may be : single-slot : single information. eg. Micrsoft CEO Bill Gates multi-slot : set of information. eg. Powai: 1 BHK Rs.7,000 p.m., 2 BHK Rs. 20,000 p.m.

  18. IE System Design Two main approaches : Knowledge engineering : grammars expressing rules are constructed by hand. specific to application. rely on the skill of knowledge engineer. Automatic training : based on training-testing paradigm. highly depend on the corpus for accuracy.

  19. Learning pattern by LP (LP)2 It learns rules by generalising over a set of examples marked via SGML tags in a training corpus It induces two types of symbolic rules: tagging rules : inserts single SGML tag. selects a tag in the training corpus and extracts from the text a window of w words to the left and w words to the right. Windows are tagged by SGML tags. Uses Context rules. eg. Seminar at 9 pm. After tagging Seminar at <time> 9 </time> pm

  20. (LP)2contd .. Correction rules: shifts wrongly positioned tags to the correct positions. Identical to the tagging rules except: patterns match also the tags inserted by the tagging rules actions shift misplaced tags rather than adding new ones.

  21. (LP)2contd .. Generalisation: It generalises the initially induced rules. Each generalisation is tested on the training corpus and the best k are retained. adding the wildcard characters like in 4 pm * pm Using POS tagger NLP knowledge

  22. (LP)2contd .. Information extraction: Information is extracted in following ways: best pool rules are used to tag. correction rules are applied to remove errors. tags are validated i.e. uncoupled tags are removed.

  23. (LP)2contd .. NLP based. [7]

  24. WHISK [6] IE Systems

  25. WHISK: • Input Data. • WHISK rule representation. • WHISK Algorithm.

  26. Input Data: • Structured data Input: eg: Profiles on web host, consider facebook, orkut etc. • Semi-Structured: eg: Ads in newspaper. • Free text: eg: Any random article. • Single-slot / Multi-slot

  27. WHISK Rules: • For Structured and semi-structured text: eg: Consider an add for renting house. The corresponding rule will be of the form: • ID:: 1 Pattern:: * ( Digit ) ‘R’ * ‚’$’ ( Number )‏ OutPut:: Rental {Bedrooms $1} {Price $2} • Disjunction allowed: Bdrm = (brs | br | bds | bdrm | bd | bedrooms | bedroom | bed)‏ • Thus the newly formed rule will be: ID:: 2 Pattern:: * ( Nghbr ) * ( Digit ) ‘ ‘ Bdrm * ‘$‘ ( Number )‏ Output:: Rental {Neighborhood $1} {Bedrooms $2} {Price $} We have used Nghbr, what does it stand for?

  28. Contd: • Extension of the rules for grammatical text: • Pre-processing required, Syntactic analysis. @S[ {SUBJ @PN[ C. Vincent Protho ]PN , @PS[ chairman and chief excutive officer ] of this maker of semiconductots, } {VB @Passive was named @nam } {PP to the additional post of @PS[ president ]PS , } {REL_V succeeding @succeed @PN[ John W. Smith ]PN , who resigned @resign to pursue @pursu other interests. } ]@S 8910130051-1 • The corresponding rule pattern will be: ID:: 3 Pattern:: *(Person)* ‘@Passive‘ *F ‘named‘ * {PP *F (Position) * ‘@succeed ‚ (Person)‏ Output:: Succession {PersonIn $1} {Post $2} {PersonOut $3}

  29. WHISK Algorithm: • Features: • A supervised learning algorithm • Tagging process is interleaved with the learning. • Presents the user with a batch of instances to tag. • Induces a set of rules from the expanded training set. • Progress: • Begins with a reservoir of untagged instances. • At each iteration a set of untagged instances are selected from the reservoir and presented to the user to annotate. • The user adds a tag for each case frame to be extracted from the instance, ‘Training Instances’

  30. Algorithm: @S[ Capitol Hill – 1 br twnhne. Fplc D/W W/D. Undrgrnd pkg incl $675. 3 BR, upper flr of turn of ctry HOME. Incl gar, grt N. Hill loc $995. (206) 999-9999 <br> <i> <font size=-2> (This ad last ran on 08/03/97.) </font> </i> <hr> ]@S 5 @@TAGS Rental {Neighborhood Capitol Hill} {Bedrooms 1} {Price 675} @@TAGS Rental {Neighborhood Capitol Hill} {Bedrooms 3} {Price 995} WHISK(Reservoir)‏ RuleSet = NULL Training = NULL Repeat at user‘s request Select a batch of NewInst from Reservoir (User tags the NewInst)‏ Add NewInst to Training Discard rules with errors on NewInst For each Inst in Training For each Tag of Inst If Tag is not covered by RuleSet Rule = GROW_RULE(Inst, Tag, Training)‏ Prune RuleSet

  31. Contd: Anchoring the extraction Slot: Empty Rule: “ * ( * ) * ( * ) * ( * ) * “ Anchoring Slot 1: Base_1: * ( Nghbr )‏ Base_2: ‘@start‘ ( * ) ‘ -‘ Anchoring Slot 2: Base_1: * ( Nghbr ) * ( Digit )‏ Base_2: * ( Nghbr ) * ‘- ‘ ( * ) ‘ br‘ Anchoring Slot 3: Base_1: * ( Nghbr ) * ( Digit ) * ( Number )‏ Base_2: * ( Nghbr ) * ( Digit ) * ‘$‘ ( * ) ‘.‘

  32. Contd: GROW_RULE(Inst, Tag, Training)‏ Rule = empty rule (terms replaced by wildcards)‏ For i = 1 to number of slots in Tag ANCHOR(Rule, Inst, Tag, Training, i)‏ Do until Rule makes no errors on Training or no improvement in Laplacian EXTEND_RULE(Rule, Inst, Tag, Training)‏ ANCHOR(Rule, Inst, Tag, Training, i)‏ Base_1 = Rule + terms just within extraction i Test first i slots of Base_1 on Training While Base_1 does not cover Tag EXTEND_RULE(Base_1, Inst, Tag, Training)‏ Base_2 = Rule + terms just outside extraction i Test first i slots of Base_2 on Training While Base_2 does not cover Tag EXTEND_RULE(Base_2, Inst, Tag, Training)‏ Rule = Base_1 If Base_2 covers more of Training than Base_1 Rule = Base_2 Laplacian = (e +1) / (n +1), where e is the number of errors and n is the number of extractions made on the training set

  33. Contd: EXTEND_RULE(Rule, Inst, Tag, Training)‏ Best_Rule = NULL Best_L = 1.0 If Laplacian of Rule within error tolerance Best_Rule = Rule Best_L = Laplacian of Rule For each Term in Inst Proposed = Rule +Term Test Proposed on Training If Laplacian of Proposed < Best_L Best_Rule = Proposed Best_L = Laplacian of Proposed Rule = Best_Rule • WHISK represents a form of hill climbing, and cannot guarantee that the rules it grows on are optimal.

  34. Interactive preparation of training • Selecting informative instances: • In each iteration of WHISK, a batch of instances is selected from the reservoir of untagged instances, presented to the user for tagging, and then added to the training set. • Classes of untagged instances: • Instances covered by an existing rule • Instances that are near misses of a rule • Instances not covered by any rule • When to stop tagging?

  35. Conclusion Till now the IR is done with the assumption of term independence, work is going on to overcome this assumption. Ability to use dynamic knowledge in IR. Visual IR and IE will improve our ability to use the WWW.

  36. References Xiaojin Zhu, “Advanced NLP: Information Retrieval”, CS838-1, WISC,2007. Dr. Ian Soboroff, ”Information Retrieval”, CMSC 491I/691I UMBC, 2002 Nazli Goharian, “Language Models”, CS429, Illinois Institute of Technology, 2007 Fabio Ciravegna, “(LP)2, an Adaptive Algorithm for Information Extraction from Web-related Texts”,IJCAI-2001 Workshop on Adaptive Text Extraction and Mining , 2001. Stephen Soderland, “Learning Information Extraction Rules for Semi-Structured and Free Text”,Machine Learning 34, 233–272 ,1999 Line Eikvil, “Information Extraction from world wide web”,A Survey. No. 945, ISBN 82-539-0429-0, pp. 39, 1999

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