Presentation is loading. Please wait.

Presentation is loading. Please wait.

Plain Text Information Extraction (based on Machine Learning ) Chia-Hui Chang Department of Computer Science & Information Engineering National Central.

Similar presentations


Presentation on theme: "Plain Text Information Extraction (based on Machine Learning ) Chia-Hui Chang Department of Computer Science & Information Engineering National Central."— Presentation transcript:

1 Plain Text Information Extraction (based on Machine Learning ) Chia-Hui Chang Department of Computer Science & Information Engineering National Central University 9/24/2002

2 Introduction Plain Text Information Extraction  The task of locating specific pieces of data from a natural language document  To obtain useful structured information from unstructured text  DARPA’s MUC program The extraction rules are based on  syntactic analyzer  semantic tagger

3 On-line documents  SRV, AAAI-1998 D. Freitag  Rapier, ACL-1997, AAAI M. E. Califf  WHISK, ML-1999 Solderland Related Work Free-text documents  PALKA, MUC-5, 1993  AutoSlog, AAAI-1993 E. Riloff  LIEP, IJCAI-1995 Huffman  Crystal, IJCAI-1995, KDD-1997 Solderland

4 SRV Information Extraction from HTML: Application of a General Machine Learning Approach Dayne Freitag AAAI-98

5 Introduction SRV  A general-purpose relational learner  A top-down relational algorithm for IE  Reliance on a set of token-oriented features Extraction pattern  First-order logic extraction pattern with predicates based on attribute-value tests

6 Extraction as Text Classification  Identify the boundaries of field instances  Treat each fragment as a bag-of-words  Find the relations from the surrounding context

7 Relational Learning Inductive Logic Programming (ILP) Input: class-labeled instances Output: classifier for unlabeled instances Typical covering algorithm  Attribute values are added greedily to a rule  The number of positive examples is heuristically maximized while the number of negative examples is heuristically minimized

8 Simple Features Features on individual token  Length (e.g. single letter or multiple letters)  Character type (e.g. numeric or alphabet)  Orthography (e.g. capitalized)  Part of speech (e.g. verb)  Lexical meaning (e.g. geographical_place)

9 Individual Predicates Individual predicate:  Length (=3): accepts only fragments containing three tokens  Some(?A [] capitalizedp true): the fragment contains some token that is capitalized  Every(numericp false): every token in the fragment is non- numeric  Position(?A fromfirst <2): the token bound to ?A is either first or second in the fragment  Relpos(?A ?B =1) the token bound to ?A immediately preceds the token bound to ?B

10 Relational Features Relational Feature types  Adjacency (next_token)  Linguistic syntax (subject_verb)

11 Example

12 Search Adding predicates greedily, attempting to cover as many positive and as few negative examples as possible. At every step in rule construction, all documents in the training set are scanned and every text fragment of appropriate size counted. Every legal predicate is assessed in terms of the number of positive and negative examples it covers. A position-predicate is not legal unless some- predicate is already part of the rule

13 Relational Paths Relational features are used only in the Path argument to the some-predicate  Some(?A [prev_token prev_token] capitalized true): The fragment contains some token preceded by a capitalized token two tokens back.

14 Validation Training Phase  2/3: learning  1/3: validation Testing  Bayesian m-estimates: All rules matching a given fragment are used to assign a confidence score. Combined confidence :

15 Adapting SRV for HTML

16 Experiments Data Source:  Four university computer science departments: Cornell, U. of Texas, U. of Washington, U. of Wisconsin Data Set:  Course: title, number, instructor  Project: title, member  105 course pages  96 project pages Two Experiments  Random: 5 cross-validation  LOUO: 4-fold experiments

17 OPD Coverage: Each rule has its own confidence

18 MPD

19 Baseline Strategies OPD MPD Simply memorizes field instances Random Guesser

20 Conclusions Increased modularity and flexibility  Domain-specific information is separate from the underlying learning algorithm Top-down induction  From general to specific Accuracy-coverage trade-off  Associate confidence score with predictions Critique: single-slot extraction rule

21 RAPIER Relational Learning of Pattern-Match Rules for Information Extraction M.E. Califf M.E. Califf and R.J. Mooney ACL-97, AAAI-1999

22 Rule Representation Single-slot extraction patterns  Syntactic information (part-of-speech tagger)  Semantic class information (WordNet)

23 The Learning Algorithm A specific to general search  The pre-filler pattern contains an item for each word  The filler pattern has one item from each word in the filler  The post-filler has one item for each word Compress the rules for each slot  Generate the least general generalization (LGG) of each pair of rules  When the LGG of two constraints is a disjunction, we create two alternatives (1) disjunction (2) removal of the constraints.

24 Example Located in Atlanta, Georgia. Offices in Kansas City, Missouri.,,,,

25 Example: Assume there is a semantic class for states, but not one for cities. Located in Atlanta, Georgia. Offices in Kansas City, Missouri.

26

27 Experimental Evaluation 300 computer-related Jobs  17 slots: employer, location, salary, job requirements, language and platform.

28 Experimental Evaluation 485 seminar announcement  4 slots:

29 WHISK: S. Soderland University of Washington Journal of Machine Learning 1999

30 Semi-structured Text

31 Free Text Person name Position Verb stem

32 WHISK Rule Representation For Semi-structured IE

33 WHISK Rule Representation For Free Text IE Person name Position Verb stem Skip only whithin the same syntactic field

34 Example – Tagged by Users

35 The WHISK Algorithm

36 Creating a Rule from a Seed Instance Top-down rule induction  Start from an empty rule  Add terms within the extraction boundary (Base_1)  Add terms just outside the extraction (Base_2)  Until the seed is covered

37 Example

38

39

40 EN

41 AutoSlog: Automatically Constructing a Dictionary for Information Extraction Tasks Ellen Riloff Dept. of Computer Science, University of Massachusetts, AAAI93

42 AutoSlog Purpose:  Automatically constructs a domain-specific dictionary for IE Extraction pattern (concept nodes):  Conceptual anchor: a trigger word  Enabling conditions: constraints

43 Concept Node Example Physical target slot of a bombing template

44 Construction of Concept Nodes 1. Given a target piece of information. 2. AutoSlog finds the first sentence in the text that contains the string. 3. The sentence is handed over to CIRCUS which generates a conceptual analysis of the sentence. 4. The first clause in the sentence is used. 5. A set of heuristics are applied to suggest a good conceptual anchor point for a concept node. 6. If none of the heuristics is satisfied, AutoSlog searches for the next sentence, and goto 3.

45 Conceptual Anchor Point Heuristics

46 Background Knowledge Concept Node Construction  Slot The slot of the answer key  Hard and soft constraints Type: Use template types such as bombing, kidnapping  Enabling condition: heuristic pattern Domain Specification  The type of a template  The constraints for each template slot

47 Another good concept node definition Perpetrator slot from a perpetrator template

48 A bad concept node definition Victim slot from a kidnapping template

49 Empirical Results Input:  Annotated corpus of texts in which the targeted information is marked and annotated with semantic tags denoting the type of information (e.g., victim) and type of event (e.g., kidnapping)  1500 texts with 1258 answer keys contain 4780 string fillers Output:  1237 concept node definitions  Human intervention: 5 user-hour to sift through all generated concept nodes  450 definitions are kept Performance:

50 Conclusion In 5 person-hour, AutoSlog creates a dictionary that achieves 98% of the performance of hand-crafted dictionary Each concept node is a single-slot extraction pattern Reasons for bad definitions  When a sentence contains the targeted string but does not describe the event  When a heuristic proposes the wrong conceptual anchor point  When CIRCUS incorrectly analyzes the sentence

51 CRYSTALCRYSTAL: Inducing a Conceptual Dictionary S. Soderland, D. Fisher, J. Aseltine, W. Lehnert University of Massachusetts IJCAI’95

52 Concept Nodes (CN) CN-type Subtype Extracted syntactic constituents Linguistic patterns Constraints on syntactic constituents

53 The CRYSTAL Induction Tool Creating initial CN definitions  For each instance Inducing generalized CN definitions  Relaxing constraints for highly similar definitions Word constraints: intersecting strings of words Class constraints: moving up the semantic hierarchy

54

55 Inducing Generalized CN Definitions 1. Start from a CN definition, D 2. Assume we have found a second definition D’ which is similar to D, a) Create a new definition U b) Delete from the dictionary all definitions covered by U, e.g. D and D’ c) Test if U extracts only marked information a) If ‘Yes’, then go to Step 2 and set D=U, b) If ‘No’, then start from another definition as D

56

57 Implementation Issue Finding similar definitions  Indexing CN definitions by verbs and by extraction buffers Similarity metric  Intersecting classes or intersecting strings of words Testing error rate of a generalized definition  A database of instances segmented by sentence analyzer is constructed

58 Experimental Results 385 annotated hospital discharge reports training instances The choice of error tolerance parameter is used to manipulate a tradeoff between precision and recall Output: CN definitions  194, coverage=10  527, 2

59 Comparison Bottom-up: From specific to generalized  CRYSTAL [Soderland, 1996]  RAPIER [Califf & Mooney, 1997] Top-down: From general to specific  SRV [Freitag, 1998]  WHISK [Soderland, 1999]

60 References I. Muslea, Extraction Patterns for Information Extraction Tasks: A Survey, The AAAI-99 Workshop on Machine Learning for Information Extraction.Extraction Patterns for Information Extraction Tasks: A Survey Riloff, E. (1993) Automatically Constructing a Dictionary for Information Extraction Tasks, AAAI-93, pp Automatically Constructing a Dictionary for Information Extraction Tasks S. Soderland, et al, CRYSTAL: Inducing a Conceptual Dictionary, AAAI-95.CRYSTAL: Inducing a Conceptual Dictionary Dayne Freitag, Information Extraction from HTML: Application of a General Machine Learning Approach, AAAI98 Information Extraction from HTML: Application of a General Machine Learning Approach Mary Elaine Califf and Raymond J. Mooney, Relational Learning of Pattern-Match Rules for Information Extraction, AAAI-99, Orlando, FL, pp , July, 1999.Relational Learning of Pattern-Match Rules for Information Extraction S. Soderland, Learning information extraction rules for semi- structured and free text. J. of Machine Learning, 1999.Learning information extraction rules for semi- structured and free text


Download ppt "Plain Text Information Extraction (based on Machine Learning ) Chia-Hui Chang Department of Computer Science & Information Engineering National Central."

Similar presentations


Ads by Google