WO2001001289A1 - Semantic processor and method with knowledge analysis of and extraction from natural language documents - Google Patents
Semantic processor and method with knowledge analysis of and extraction from natural language documents Download PDFInfo
- Publication number
- WO2001001289A1 WO2001001289A1 PCT/US2000/017444 US0017444W WO0101289A1 WO 2001001289 A1 WO2001001289 A1 WO 2001001289A1 US 0017444 W US0017444 W US 0017444W WO 0101289 A1 WO0101289 A1 WO 0101289A1
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- WIPO (PCT)
- Prior art keywords
- sao
- storing
- subject
- association
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/232—Orthographic correction, e.g. spell checking or vowelisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
Definitions
- the present invention relates to natural language processing systems, and more specifically to a method and system for converting natural language texts into Subject-
- Action-Object Knowledge Database (SAO KB). This database can form the heart of various new applications or methods of natural language processing and analysis.
- parsers are included in known natural language processors, such as Ergo Linguistic Technologies parser (U.S. Pat. No. 5878385), which
- Part-of-Speech (POS) identification has the following features: Part-of-Speech (POS) identification; Parts of Sentences identification; Passive to Active and Active to Passive mode conversion; Statement to
- verb chains Subjects, verb chains and objects of the sentence are extracted syntactically but not semantically. As a result, semantic actions (verb chains) can be recognized only if they are described by finite verbs and generally can not be recognized if the actions are described
- a general purpose computer including entering and storing a user criterion, entering into a
- first storage area representations of the texts of a plurality of natural language documents that have some relationship with the stored user criterion formatting said representations and storing the formatted text in a second storage area, identifying and extracting from the
- solutions comprising the subject portions of the one or more stored lemmatized SAO
- Figure 1 is a pictorial representation of one exemplary embodiment of the system according to the principles of the present invention.
- Figure 2 is a schematic representation of the main architectural elements of the system and functional links according to the present invention.
- Figure 3 is a structural and functional schematic representation of Unit 18 of Figure 2.
- Figure 4 is a structural and functional schematic representation of Unit 20 of Figure 2.
- Figure 5 is a structural and functional schematic representation of Unit 22 of Figure 2.
- Figure 6 is a schematic representation of Unit 42 of Figure 4.
- Figure 7 is a schematic representation of Unit 44 of Figure 4.
- Figure 8 is a schematic representation of Unit 46 of Figure 4.
- Figure 9 is a schematic representation of Unit 26 of Figure 2.
- Figure 10 is a typical example of the text to be semantically processed.
- Figure 11 is a representation of formatted text of Figure 10.
- Figure 12 is a representation of error corrected text of Figure 11.
- Figure 13 is a representation of word-splitted text of Figure 12.
- Figure 14 is a representation of sentence-splitted text of Figure 13.
- Figure 15 is a representation of tagged text of Figure 14.
- Figure 16 is a representation of parsed text of Figure 15.
- Figure 17 is a representation of SAO DB extracted from parsed text of Figure 16.
- Figure 18 is a representation of lemmatized SAO DB of Figure 17.
- Figure 19 is a typical example of relevant SAO DB entry of Figure 18.
- Figure 20 is a representation of a Problem Folder generated in response to the
- Figure 21 A is a representation of three original input texts from various sources.
- Figure 21B is a representation of the output structured SAO KB resulting from
- a CPU 4 with MODEM and or cable box 5 that could comprise a general purpose computer or networked server or minicomputer with standard user input and output device such as keyboard 10, mouse 8, printer 6 and monitor 2 and/or other user data entry device 9.
- the SAO Semantic Processor (Fig.2) includes a
- Preformator 18 receives the document data 28 from the database 16, removes formatting symbols and other symbols that are not part of natural language text (Unit 30),
- Unit 30 in preformator 18 removes from the input text 28 all the
- the Preformator splits the text into words (Unit 34) and sentences.
- SAO Extractor Unit 20 tags the text with part-of-speech tags (Unit 42), parses (Unit 44) the text 40 syntactically, recognizes Subjects, Actions and Objects, their attributes, Cause-Effect relations between SAO-triplets and builds the Syntactical Tree of each sentence of the text 40 (unit 46), which then outputs to the SAO Editor (Unit 22).
- the Preformator supplies the Formatted text 38 to the input of the SAO Extractor (Fig.4).
- SAO Extractor uses Linguistic Knowledge base in order to tag the Formatted text 40 with part-of-speech tags (Unit 42). There are preferably three stages in POS tagging process.
- a context-independent analysis module assigns each word of the text 66 a set of one or more part-of-speech tags. Then the disambiguation context-dependent
- Unit 70 uses statistical Hidden Markov Model algorithm to assign each word of the text a unique part-of-speech tag (unit 70).
- Unit 72 uses a rule-based POS tagging module to perform the correction of the output of the Unit 70 and recognition of unknown
- the parsed text 86 is supplied to the Unit 46 which extracts SAOs from the
- Parsed text 90 (Fig. 8). At first, SAOs with finite verbs as Actions are extracted where Action type recognized in Unit 100 enables Unit 102 to extract Subject and Object from
- Unit 104 is recognized in Unit 104 and as verbal nouns recognized in Unit 106. All Subjects and Objects attributes (location, composition, etc.) are recognized in Unit 108. Next, Unit 109 recognizes Cause-Effect relations for SAO-triplets. As the result, the SAO Extractor
- SAO Editor Unit 22 ( Figures 2 and 5) performs the lemmatization of Actions (unit
- SAO Editor provides the possibility to filter SAOs, i.e. to remove from SAO database SAOs (Unit 52) not
- the resulting SAO Knowledge Base (Unit 24) includes SAO database and various tools for analyzing SAOs and building
- Linguistic Knowledge Base includes Database section (1) and Database of Recognizing Linguistic Models section (2), which describes algorithms for recognizing linguistic objects and relations in the text.
- Preformator (Unit 18) accesses and is controlled by information stored in blocks (3), (4), (5), (10), (12), (13), (14).
- SAO Extractor (Unit 20) accesses and is controlled by
- the method and apparatus of the present invention provide the user with the possibility of automatically extracting World Knowledge from text and storing it in the
- SAOs where SAOs can be lemmatized and unified into complex hierarchical structures using their attributes and meanings which in turn can help extract other types of
- the classifier contains a list of tags which are traditionally called part-of-speech
- tags The list includes tags for nouns, verbs, adjectives, adverbs, prepositions, etc. But
- NNS common noun, plural
- JJ adjective
- each stored word is linked with a set of part-of-speech
- the Idiomatic Dictionary comprises set expressions and idioms. Each idiom or unit is assigned a part-of-speech tag or a set of
- part-of-speech tags e.g. go into detail — VB a great deal of — ABL
- idioms contains 2200 idioms. It is well known that part of speech properties of idioms can not be obtained by analyzing words that constitute idioms. So, the use of idioms can dramatically
- objects of the outer world i.e. inherent properties (features) of an object measured by a
- This Dictionary contains in one exemplary embodiment of the present invention about 1250 parameters.
- syntactic classes which are used for classification of structural elements of syntactically analyzed sentences which are optimized for further SAO extraction.
- This Probabilistic Grammar provides means for automatically annotating the text with part of
- the algorithm is based on the known Hidden Markov Model and uses statistical data from block 12.
- This Rule-Based Grammar is used as the final step of part-of-speech tagging process.
- the Linguistic Facts module contains Filters Database, Dictionary Word-Code-
- Filters database includes a list of lexical items and their codes which are considered to be non-informative by knowledge engineers. This information is used by SAO Editor (Fig.5) which checks if it should
- the Error Detection and Correction module contains Recognizing Linguistic
- Unit 32 (Fig.3) uses Recognizing Linguistic Models in
- the Probabilistic Grammar, Unit (10) calculates the most likely word from the above mentioned set of words and corrects the spelling error automatically. If the
- the Unit (14) uses formal characteristics like spaces, capital letters and punctuation for determining word and sentence boundaries.
- the splitter is used by Preformator ( Figure 3).
- Each idiom is assigned a part of speech tag from a list of tags that it can have. The algorithm tends to recognize the longest idiom with a given first word.
- This Recognizer includes Recognizing Linguistic Models for Verb Chains Recognition. These Models use part-of-speech tagged text (Unit 78) and rules for
- This Recognizer includes Linguistic Models for Noun Group Recognition. They can also be described in Backus Naur Form. Noun group recognition rules use part-of-
- speech tagged text and lexemes (such as prepositions, conjunctions and adverbs) in order to extract noun groups, keeping the information on internal structure of noun groups, which is used during next steps of SAO analysis(Subject and Object extraction, Subject and Object lemmatization).
- This module includes are stored Recognizing Linguistic Models for Functional and
- Syntactic Phrase Tree Construction They describe rules for structurization of the sentence, i.e. for correlating part-of-speech tags, syntactic and semantic classes, etc. which
- Action and Object They describe rules that use part-of-speech tags, lexemes and syntactic categories which are then used by SAO Editor (Fig. 5) while lemmatizing Actions (unit 54), Subjects and Objects (Unit 56).
- SAOs These models use linguistic patterns, lexemes and predefined codes from a list of codes. These patterns describe the location of cause and effect in the input sentence.
- noun group e.g.
- the network termination unit includes a plurality of semi-conductor switches electrically connected to conductors of the telephone line to establish a network of electrical paths capable of altering the electrical conduction of the telephone line when caused to assume a state of conduction.
- SAO extraction module ( Fig.8, Unit 109).
- FIGS 10-19 show the results of various process steps designated in the respective figure for the sentence:
- the pressure-sensitive device moves the air through the conducting lumen and into the intubated patient's airway.
- Figure 20B shows the Problem Folder for this task with each of the four possible
- Action is the constituent that is expressed either by a finite verb or non-finite verb or
- verbal noun and denotes a relation between Subject and Object.
- corpus a collection of text in machine-readable form
- lemmatization the process or result of dividing a text into sets of different forms of a
- Linguistic KB (knowledge base): a database of (i) Recognition Linguistic Models and
- Object is the constituent that is affected by the Action, e.g. John likes Mary. Object is
- parser toll (often automatic or semi-automatic computer program) used for parsing
- POS part-of-speech
- part-of-speech tagging assigning part-of-speech tags to a text.
- part-of-speech tag a label associated with a word (or other unit) providing
- run can be tagged as a noun (run NN) or verb (run VB).
- SAO-DB is a database of S AO-triples and semantic relations.
- SAO-KB (knowledge base): includes SAO-DB, set of rules for structurizing SAO-
- SAO Triple SAO-triplet
- SAO Triplet is a set of Subject, Action and Object, related one with another.
- SAOs e.g. relations like Object-Parameter
- SAO as a whole e.g. relations like
- Storage Area either a separate storage facility in a general purpose computer or
- Subject is the constituent that performs the Action, e.g. John likes Mary. Subject is
- Subject Attributes is a property of a Subject (Object), e.g.
- Syntactic Tree of Sentence is a tree view of the sentence where nodes are syntactic
- tag-classifier set of tags used for part of speech tagging.
Abstract
Description
Claims
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU56370/00A AU5637000A (en) | 1999-06-30 | 2000-06-23 | Semantic processor and method with knowledge analysis of and extraction from natural language documents |
EP00941702A EP1208457A1 (en) | 1999-06-30 | 2000-06-23 | Semantic processor and method with knowledge analysis of and extraction from natural language documents |
Applications Claiming Priority (2)
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US34554799A | 1999-06-30 | 1999-06-30 | |
US09/345,547 | 1999-06-30 |
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WO2001001289A1 true WO2001001289A1 (en) | 2001-01-04 |
WO2001001289A8 WO2001001289A8 (en) | 2001-06-21 |
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PCT/US2000/017444 WO2001001289A1 (en) | 1999-06-30 | 2000-06-23 | Semantic processor and method with knowledge analysis of and extraction from natural language documents |
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EP (1) | EP1208457A1 (en) |
AU (1) | AU5637000A (en) |
WO (1) | WO2001001289A1 (en) |
Cited By (12)
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WO2003077154A2 (en) * | 2002-03-14 | 2003-09-18 | Universita'degli Studi Di Firenze | System and method for performing functional analyses making use of a plurality of inputs |
GB2417103A (en) * | 2004-08-11 | 2006-02-15 | Sdl Plc | Natural language translation system |
ITTO20120303A1 (en) * | 2012-04-05 | 2012-07-05 | Wolf S R L Dr | METHOD AND SYSTEM FOR CARRYING OUT ANALYSIS AND AUTOMATIC COMPARISON OF PATENTS AND TECHNICAL DESCRIPTIONS. |
US9128929B2 (en) | 2011-01-14 | 2015-09-08 | Sdl Language Technologies | Systems and methods for automatically estimating a translation time including preparation time in addition to the translation itself |
US9569425B2 (en) | 2013-03-01 | 2017-02-14 | The Software Shop, Inc. | Systems and methods for improving the efficiency of syntactic and semantic analysis in automated processes for natural language understanding using traveling features |
EP3316148A1 (en) * | 2016-10-30 | 2018-05-02 | Wipro Limited | Method and system for determining action items from knowledge base for execution of operations |
US10198438B2 (en) | 1999-09-17 | 2019-02-05 | Sdl Inc. | E-services translation utilizing machine translation and translation memory |
US10248650B2 (en) | 2004-03-05 | 2019-04-02 | Sdl Inc. | In-context exact (ICE) matching |
CN109918640A (en) * | 2018-12-22 | 2019-06-21 | 浙江工商大学 | A kind of Chinese text proofreading method of knowledge based map |
US10635863B2 (en) | 2017-10-30 | 2020-04-28 | Sdl Inc. | Fragment recall and adaptive automated translation |
US10817676B2 (en) | 2017-12-27 | 2020-10-27 | Sdl Inc. | Intelligent routing services and systems |
US11256867B2 (en) | 2018-10-09 | 2022-02-22 | Sdl Inc. | Systems and methods of machine learning for digital assets and message creation |
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US20040001099A1 (en) * | 2002-06-27 | 2004-01-01 | Microsoft Corporation | Method and system for associating actions with semantic labels in electronic documents |
US8521506B2 (en) | 2006-09-21 | 2013-08-27 | Sdl Plc | Computer-implemented method, computer software and apparatus for use in a translation system |
US9262403B2 (en) | 2009-03-02 | 2016-02-16 | Sdl Plc | Dynamic generation of auto-suggest dictionary for natural language translation |
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- 2000-06-23 EP EP00941702A patent/EP1208457A1/en not_active Withdrawn
- 2000-06-23 WO PCT/US2000/017444 patent/WO2001001289A1/en not_active Application Discontinuation
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US10198438B2 (en) | 1999-09-17 | 2019-02-05 | Sdl Inc. | E-services translation utilizing machine translation and translation memory |
US10216731B2 (en) | 1999-09-17 | 2019-02-26 | Sdl Inc. | E-services translation utilizing machine translation and translation memory |
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WO2003077154A3 (en) * | 2002-03-14 | 2004-04-08 | Univ Firenze | System and method for performing functional analyses making use of a plurality of inputs |
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US10248650B2 (en) | 2004-03-05 | 2019-04-02 | Sdl Inc. | In-context exact (ICE) matching |
GB2417103A (en) * | 2004-08-11 | 2006-02-15 | Sdl Plc | Natural language translation system |
US9128929B2 (en) | 2011-01-14 | 2015-09-08 | Sdl Language Technologies | Systems and methods for automatically estimating a translation time including preparation time in addition to the translation itself |
ITTO20120303A1 (en) * | 2012-04-05 | 2012-07-05 | Wolf S R L Dr | METHOD AND SYSTEM FOR CARRYING OUT ANALYSIS AND AUTOMATIC COMPARISON OF PATENTS AND TECHNICAL DESCRIPTIONS. |
US9965461B2 (en) | 2013-03-01 | 2018-05-08 | The Software Shop, Inc. | Systems and methods for improving the efficiency of syntactic and semantic analysis in automated processes for natural language understanding using argument ordering |
US9594745B2 (en) | 2013-03-01 | 2017-03-14 | The Software Shop, Inc. | Systems and methods for improving the efficiency of syntactic and semantic analysis in automated processes for natural language understanding using general composition |
US9569425B2 (en) | 2013-03-01 | 2017-02-14 | The Software Shop, Inc. | Systems and methods for improving the efficiency of syntactic and semantic analysis in automated processes for natural language understanding using traveling features |
EP3316148A1 (en) * | 2016-10-30 | 2018-05-02 | Wipro Limited | Method and system for determining action items from knowledge base for execution of operations |
US10635863B2 (en) | 2017-10-30 | 2020-04-28 | Sdl Inc. | Fragment recall and adaptive automated translation |
US11321540B2 (en) | 2017-10-30 | 2022-05-03 | Sdl Inc. | Systems and methods of adaptive automated translation utilizing fine-grained alignment |
US10817676B2 (en) | 2017-12-27 | 2020-10-27 | Sdl Inc. | Intelligent routing services and systems |
US11475227B2 (en) | 2017-12-27 | 2022-10-18 | Sdl Inc. | Intelligent routing services and systems |
US11256867B2 (en) | 2018-10-09 | 2022-02-22 | Sdl Inc. | Systems and methods of machine learning for digital assets and message creation |
CN109918640A (en) * | 2018-12-22 | 2019-06-21 | 浙江工商大学 | A kind of Chinese text proofreading method of knowledge based map |
CN109918640B (en) * | 2018-12-22 | 2023-05-02 | 浙江工商大学 | Chinese text proofreading method based on knowledge graph |
Also Published As
Publication number | Publication date |
---|---|
AU5637000A (en) | 2001-01-31 |
WO2001001289A8 (en) | 2001-06-21 |
EP1208457A1 (en) | 2002-05-29 |
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