CS4025: Advanced Information Extraction. Overview CS4025, Department of Computing Science, University of Aberdeen 2 Overview of aspects of IE and General

CS4025:

Advanced Information Extraction

CS4025, Department of Computing Science, University of Aberdeen

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Overview

• Overview of aspects of IE and General Architecture for Text Engineering (GATE)

• Examples


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Main Point

• Context: Textual material is expressed in natural language. We understand the structure and the meaning of textual material, but it is unstructured information for a machine.

• Problem: How to structure the information to support processing – information extraction for queries, reasoning, and further machine processing?

• Solution: Annotate the data with semantic mark ups using natural language processing systems. Makes data machine readable.


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Problems for Annotation

• Annotate large legacy corpora.

• Address growth of corpora.

• Distribution of information.

• Reduce number of human annotators and tedious work.

• Make annotation systematic, automatic, and consistent.

• Annotate fine-grained information: names, locations, addresses, organisations, actions, relations amongst terms.


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An Approach

• Knowledge heavy, using lists, rules, and processes. Labour and knowledge intensive. Transparent.

• Decompose large complex problems into smaller, manageable problems for which we can create solutions.

• Make implicit information explicit by adding machine readable annotations.

• Software engineering approach.


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Development Cycle

Source Text

Linguistic Analysis

Tool Construction

Knowledge Extraction

Evaluation


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Computational Linguistic Cascade I

• Sentence segmentation - divide text into sentences.

• Tokenisation - words identified by spaces between them.

• Lemmatisation – homogenise 'run', 'ran', 'runs' to 'run'.

• Part of speech tagging - noun, verb, adjective....

• Morphological analysis - singular/plural, tense, nominalisation, ...

• Shallow syntactic parsing/chunking - noun phrase, verb phrase, subordinate clause, ....

• Identification of relevant terms and constructions.


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Computational Linguistic Cascade II

• Named entity recognition - the entities in the text.

• Dependency analysis - subordinate clauses, pronominal anaphora,...

• Relationship recognition – X is president of Y; A hit B with a car and killed B.

• Enrichment - add lexical semantic information to verbs or nouns.

• Each step guided by pattern matching and rule application.


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GATE

• General Architecture for Text Engineering (GATE) - open source framework which supports plug-in NLP components to process a corpus of text.

• GATE Training Courses 2014http://gate.ac.uk/wiki/TrainingCourseJune2014/

• A GUI to work with the tools.• A Java library to develop further applications.• Components and sequences of processes, each process

feeding the next in a “pipeline”.• Annotated text output or other sorts of output.


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Methodology I

• Form the corpus of text.• Identify terminology and sort into 'classes'. Maybe use a

spreadsheet for development.• Put sorted terminology into gazetteer lists (GAZ).• Create JAPE rules to 'reveal' the terminology.• Run the pipeline.• Examine results either 'in situ' or query with semantic search.• Refine/revise lists, rules, and queries.• Add further GATE processing modules as needed.


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Methodology II


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Basic Process Flow


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Example Process Flow


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Gazetteers

• Gazetteers are lookup lists that add features - when a string in the text is located in a lookup list, annotate the string in the text with the feature. Conceptual covers.

• Feature: list of items...

• Obligation: ought, must, obliged, obligation....• Exception: unless, except, but, apart from....• Verbs according to thematic roles: lists of verbs and their

associated roles, e.g. run has an agent (Bill ran), rise has a theme (The wind blew).

• Easy to change.


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JAPES

• JAPE Rules (finite state transduction rules) create overt annotations and reuse other annotations (e.g. Parser Output):

• Easy to change.


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Example 1Psychology


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Corpus


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Terminology


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Spreadsheet

Facilitates adding, sorting, classifying.... the terminology.


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GAZ


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JAPE


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Pipeline


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Results in situ


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Results with ANNIC (one)


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Results with ANNIC (more)


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Other Modules (POS, Sentiment, etc.)


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Example 2Rule Extraction from Regulations

Wyner, LEX 2013, Ravenna, Italy (cc) by-nc-sa license

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• Identify and extract rules from regulations using a rule-based, bottom-up, linguistically expressive, open-source, verifiable tool.

• Fine-grained structure to identify rule structure, nouns with their thematic roles, exceptions, and lists.

• Carry out an experiment on a portion of a regulation, demonstrating the feasibility of the approach and tools.

• Results (start to be) useful for knowledge acquisition and engineering.

• Towards computational semantics of natural language.

Main Points

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Use Cases

• Legislation and regulations have rules that must be identified to:

• create and maintain up to date rule books that are used in compliance management, where a company must comply with the rules. (ComplianceTrack)

• create logic programs that, given input of ground facts, can generate determinations, e.g. whether an individual is due a benefit from the government or owes taxes. (Oracle)

• exchange machine readable rules. (LegalRuleML)

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However....

• The knowledge engineering bottleneck:

It is knowledge, time, and labour intensive to identify, organise, and formalise rules which are expressed in natural language into rules that can be automatically processed.

• Solution - apply Natural Language Processing tools.

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Research Material

US Code of Federal Regulations, US Food and Drug Administration, Department of Health and Human Services regulation for blood banks on testing requirements for communicable disease agents in human blood, Title 21 part 610 section 40. 4 page document of 1,777 words.

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Why Not Parse and Be Done?

• Applied the Stanford Parser, and it outputs:

• parses – sequences of words that form a grammatical phrase;

• dependencies – relationships between phrases, e.g. subject of verb;

• alternative parses.

• It failed to parse the whole text. Succeeds on portions, but still lots of issues.

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Whazza Problem?

• long, complex sentences;• alternative parses;• lists with punctuation;• references with punctuation;• embedded clauses (...that....; ...to be....);• diathesis (e.g. active-passive) and thematic roles (e.g. agent):

You must test the blood; the blood must be tested by you.

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Proposition

• Define and target a more specific task.

• Work with simpler materials and build up from there.

• Develop a knowledge-based system to identify and extract information.

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Target Model

Deontic rules:

Conditional rules:

Start with this. In future work, add punctuation, negation, temporal phrases, generics, Hohfeldian relations, tense in conditionals, references, and so on....06/09/2013


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Methodology - Materials

Given the problems of working with the source material directly, the data is systematically decomposed into less complex forms:

• Source – original materials, unparseable.• Source Sections – sections of source material, parseable,

but complex and inaccurate.• Source Derived – edited confounding issues such as long

conjunctive sentences, embeddings, and references. Created a Gold Standard in which we annotated the 'correct' elements and parses.

• Testing Data – simplified materials focusing on elements of the model.

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Source Derived

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Gold Standard

The Gold Standard encodes knowledge.06/09/2013


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Testing Data

A. B.

C.

D.

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Methodology – Modules and Materials

Develop modules for Testing Data

Apply modules to Source Derived

Test modules onGold Standard

Identify problems and refine modules

Apply modules to SD and GS....

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General Architecture for Text Engineering

NLP pipeline

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• Gazetteers are lookup lists that add features - when a string in the text is located in a lookup list, annotate the string in the text with the feature. Conceptual covers.

• Feature: list of items...

• Obligation: ought, must, obliged, obligation....• Exception: unless, except, but, apart from....• Verbs according to thematic roles: lists of verbs and

their associated roles, e.g. run has an agent (Bill ran), rise has a theme (The wind blew).

• Easy to change.06/09/2013


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• JAPE Rules (finite state transduction rules) create overt annotations and reuse other annotations (e.g. Parser Output):

• Easy to change.

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• Have Gazetteer lists and JAPE rules for:

• lists in various forms;• exception phrases in various forms;• conditionals in various forms;• deontic terms;• associating grammatical roles (e.g. subject and object)

with thematic roles (agent and theme) in various forms.

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Sample Outputs

Consequence, list structure, and conjuncts of the antecedent.

Exception, agent NP, deontic concept, active main verb, theme.06/09/2013


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Sample Output

Theme, deontic modal, passive verb, agent with complex relative clause.

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Sample Output - Overall

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Sample Output - XML

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Sample Output – ANNIC Search

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Documents

CS4025: Advanced Information Extraction. Overview CS4025, Department of Computing Science, University of Aberdeen 2 Overview of aspects of IE and General