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Empirical Methods in Information Extraction - Claire Cardie 자연어처리연구실 한 경 수 1999. 11. 2.

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Presentation on theme: "Empirical Methods in Information Extraction - Claire Cardie 자연어처리연구실 한 경 수 1999. 11. 2."— Presentation transcript:

1 Empirical Methods in Information Extraction - Claire Cardie 자연어처리연구실 한 경 수 1999. 11. 2.

2 Empirical Methods in Information Extraction[Cardie97]2 Contents q Introduction q The Architecture of an Information Extraction System q The Role of Corpus-Based Language Learning Algorithms q Learning Extraction Patterns q Coreference Resolution and Template Generation q Future Directions

3 Empirical Methods in Information Extraction[Cardie97]3 Introduction(1/2) q Information Extraction System  inherently domain specific  takes as input an unrestricted text and summarizes the text with respect to a prespecified topic or domain of interest. (Figure 1)Figure 1  skim a text to find relevant sections and then focus only on these sections.  MUC performance evaluation  recall  precision  applications  analyzing… 3 terrorist activities, business joint ventures, medical patient records, …  building… 3 KB from web pages, job listing DB from newsgroups / web sites / advertisements, weather forecast DB from web pages,... (# correct slot-fillers in output template) / (# slot-fillers in answer key) (# correct slot-fillers in output template) / (# slot-fillers in output template)

4 Empirical Methods in Information Extraction[Cardie97]4 Introduction(2/2) q Problems in today’s IE systems  accuracy  the errors of an automated IE system are … 3 due to its relative shallow understanding of the input text 3 difficult to track down and to correct  portability  domain-specific nature  manually modifying and adding domain-specific linguistic knowledge to an existing NLP system is slow and error-prone. We will see that empirical methods for IE are corpus-based, machine learning algorithms.

5 Empirical Methods in Information Extraction[Cardie97]5 The Architecture of an IE System(1/2) q Approaches to IE in the early days  traditional NLP techniques vs. keyword matching techniques q Standard architecture for IE systems (Figure 2)Figure 2  tokenization and tagging  tag each word with respect to POS and possibly semantic class  sentence analysis  one or more stages of syntactic analysis  identify… 3 noun/verb groups, prepositional phrases, subjects, objects, conjunctions, … 3 semantic entities relevant to the extraction topic  the system need only perform partial parsing 3 looks for fragments of text that can be reliably recognized 3 the ambiguity resolution decisions can be postponed

6 Empirical Methods in Information Extraction[Cardie97]6 The Architecture of an IE System(2/2) q Standard architecture for IE systems (continued)  extraction  the first entirely domain-specific component  identifies domain-specific relations among relevant entities in the text  merging  coreference resolution, or anaphora resolution 3 determines whether it refers to an existing entity or whether it is new  determine the implicit subjects of all verb phrases 3 discourse-level inference  template generation  determines the number of distinct events in the text  maps the individually extracted pieces of information onto each event  produces output templates  the best place to apply domain-specific constraint  some slots require set fills, or require normalization of their fillers.

7 Empirical Methods in Information Extraction[Cardie97]7 The Role of Corpus-Based Language Learning Algorithms(1/3) q Q: How have researchers used empirical methods in NLP to improve the accuracy and portability of IE systems?  A: corpus-based language learning algorithms have been used to improve individual components of the IE system. q For language tasks that are domain-independent and syntactic  annotated corpora already exist  POS tagging, partial parsing, WSD  the importance of WSD for IE task remains unclear. q NL learning techniques are more difficult to apply to subsequent stages of IE.  learning extraction patterns, coreference resolution, template generation

8 Empirical Methods in Information Extraction[Cardie97]8 The Role of Corpus-Based Language Learning Algorithms(2/3) q The problems of applying empirical methods  no corpora annotated with the appropriate semantic & domain-specific supervisory information  corpus for IE =  the output templates … 3 say nothing about which occurrence of the string is responsible for the extraction 3 provide no direct means for learning patterns to extract symbols not necessarily appearing anywhere in the text(set fills)  the semantic & domain-specific language-processing skills require the output of earlier levels of analysis(tagging & partial parsing).  complicate to generate the training examples  whenever the behavior of these earlier modules changes, 3 new training examples must be generated 3 the learning algorithms for later stages must be retrained  learning algorithms must deal with noise caused by errors from earlier components  new algorithms need to be developed

9 Empirical Methods in Information Extraction[Cardie97]9 The Role of Corpus-Based Language Learning Algorithms(3/3) q Data-driven nature of corpus-based approaches  accuracy  when the training data is derived from the same type of texts that the IE system is to process, 3 the acquired language skills are automatically tuned to that corpus, increasing the accuracy of the system.  portability  because each NLU skill is learned automatically rather than being manually coded, 3 that skill can be moved quickly from one IE system to another by retraining the appropriate component.

10 Empirical Methods in Information Extraction[Cardie97]10 Learning Extraction Patterns(1/5) q The role for empirical methods in the Extraction phase  knowledge acquisition: to automate the acquisition of good extraction patterns q AutoSlog[Riloff 1993]  learns extraction patterns in the form of domain-specific concept node definitions for use with the CIRCUS parser. (Figure 3)Figure 3  learns concept node definitions via a one-shot learning algorithmlearning algorithm  background knowledge  a small set of general linguistic patterns (approximately 13)  requires human feedback loop, which filters bad extraction patterns  accuracy: 98%, portability: 5 hours  critical step towards building IE systems that are trainable entirely by end-users (Figure 4)Figure 4

11 Empirical Methods in Information Extraction[Cardie97]11 Learning Extraction Patterns(2/5) Given: a noun phrase to be extracted 1. Find the sentence from which the noun phrase originated. 2. Present the sentence to the partial parser for processing. 3. Apply the linguistic patterns in order. 4. When a pattern applies, generate a concept node definition from the matched constituents, their context, the concept type provided in the annotation for the target noun phrase, and the predefined semantic class for the filler. followed by = Concept = of > Trigger = “ of >” Position = direct-object Constraints = (( of >)) Enabling Conditions = ((active-voice)) AutoSlog’s Learning Algorithm

12 Empirical Methods in Information Extraction[Cardie97]12 Learning Extraction Patterns(3/5) q PALKA[Kim & Moldovan 1995]  background knowledge  concept hierarchy 3 a set of keywords that can be used to trigger each pattern 3 comprises a set of generic semantic case frame definitions for each type of information to be extracted  semantic class lexicon q CRYSTAL[Soderland 1995]  triggers comprise a much more detailed specification of linguistic context  employs a covering algorithmcovering algorithm  medical diagnosis domain  precision: 50-80%, recall: 45-75%

13 Empirical Methods in Information Extraction[Cardie97]13 Learning Extraction Patterns(4/5) 1. Begin by generating the most specific concept node possible for every phrase to be extracted in the training texts. 2. For each concept node C 2.1. Find the most similar concept node C’. 2.2. Relax the constrains of each just enough to unify C and C’. 2.3. Test the new extraction pattern P against the training corpus. If (error rate < threshold) then Add P; Replace C and C’ else stop. CRYSTAL’s Learning Algorithm

14 Empirical Methods in Information Extraction[Cardie97]14 Learning Extraction Patterns(5/5) q Comparison  AutoSlog  general to specific  human feedback  PALKA  generalization & specialization  automated feedback  require more background knowledge  CRYSTAL  specific to general(covering algorithm)  automated feedback  require more background knowledge q Research issues  handling set fills  type of the extracted information  evaluation  determining which method for learning extraction patterns will give the best results in a new extraction domain

15 Empirical Methods in Information Extraction[Cardie97]15 Coreference Resolution and Template Generation(1/3) q Discourse processing is a major weakness of existing IE system  generating good heuristics is challenging  assume as input fully parsed sentences  must take into account the accumulated errors  must be able to handle the myriad forms of coreference across different domains q Coreference problem as a classification task (Figure 5)Figure 5  given two phrases and the context in which they occur,  classify the phrases with respect to whether or not they refer to the same object

16 Empirical Methods in Information Extraction[Cardie97]16 Coreference Resolution and Template Generation(2/3) q MLR[Aone & Bennett 1995]  use C4.5 decision tree induction system  tested on the Japanese corpus for the business joint ventures  use automatically generated data set  66 domain-independent features  evaluated using data sets derived from 250 texts  recall: 67-70 %, precision: 83-88% q RESOLVE[McCarthy & Lehnert 1995]  use C4.5 decision tree induction system  tested on the English corpus for the business joint ventures(MUC-5)  use manually generated, noise-free data set  include domain-specific features  evaluated using data sets derived from 50 texts  recall: 80-85%, precision: 87-92%

17 Empirical Methods in Information Extraction[Cardie97]17 Coreference Resolution and Template Generation(3/3) q The results for coreference resolution are promising  possible to develop automatically trainable coreference systems that can compete favorably with manually designed systems  specially designed learning algorithms need not be developed  symbolic ML techniques offer a mechanism for evaluating the usefulness of different knowledge sources q Still, much research remains to be done  additional types of anaphors using a variety of feature sets  the role of domain-specific information for coreference resolution  the relative effect of errors from the preceding phases of text analysis q Trainable systems that tackle Merging & Template Generation  TTG[Dolan 1991], Wrap-Up[Soderland & Lehnert 1994]  generate a series of decision tree

18 Empirical Methods in Information Extraction[Cardie97]18 Future Directions q Unsupervised learning algorithms  a means for sidestepping the lack of large, annotated corpora q Techniques that allow end-users to quickly train IE systems  through interaction with the system over time  without intervention by NLP system developers

19 Empirical Methods in Information Extraction[Cardie97]19 IE System in the Domain of Natural Disasters

20 Empirical Methods in Information Extraction[Cardie97]20 Architecture for an IE System

21 Empirical Methods in Information Extraction[Cardie97]21 Concept Node for Extracting “Damage” Information Concept Node Definition: domain-specific semantic case frame (one slot per frame) Concept: the type of concept to be recognized Trigger: the word that activates the pattern Position: the syntactic position where the concept is expected to be found Constraint: selectional restrictions that apply to any potential instance of the concept Enabling Conditions: constraints on the linguistic context of the triggering word that must be satisfied before the pattern is activated

22 Empirical Methods in Information Extraction[Cardie97]22 Learning Information Extraction Patterns

23 Empirical Methods in Information Extraction[Cardie97]23 A Machine Learning Approach to Coreference Resolution

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