CN106897266A - For the text handling method and system of intelligent robot - Google Patents

For the text handling method and system of intelligent robot Download PDF

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CN106897266A
CN106897266A CN201710082574.1A CN201710082574A CN106897266A CN 106897266 A CN106897266 A CN 106897266A CN 201710082574 A CN201710082574 A CN 201710082574A CN 106897266 A CN106897266 A CN 106897266A
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text
normalization
text data
core word
similarity
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魏晨
韦克礼
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Beijing Guangnian Wuxian Technology Co Ltd
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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
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3334—Selection or weighting of terms from queries, including natural language queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3343—Query execution using phonetics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/284—Lexical analysis, e.g. tokenisation or collocates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

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  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Acoustics & Sound (AREA)
  • Human Computer Interaction (AREA)
  • Machine Translation (AREA)

Abstract

The invention discloses a kind of text handling method and text processing system for intelligent robot.The intelligent robot is provided with robot operating system, and the method includes：Obtain pending text data；Core word extraction is carried out to the pending text data by syntactic analysis, being compared based on core word carries out Text normalization treatment, wherein, the text data to comparing normalization failure based on core word carries out Text normalization based on Arithmetic of Semantic Similarity；Output and/or storage Text normalization result.The inventive method can improve intelligent robot and the interaction capabilities with user, preferable Text normalization treatment can be carried out to pending text data, it is easy to follow-up Language Processing, it is possible to increase the language interaction capabilities of intelligent robot, better meets user's request.

Description

For the text handling method and system of intelligent robot
Technical field
The present invention relates to field in intelligent robotics, more particularly to a kind of text handling method for intelligent robot and it is System.
Background technology
With continuing to develop for science and technology, the introducing of information technology, computer technology and artificial intelligence technology, machine Industrial circle is progressively walked out in the research of people, gradually extend to the neck such as medical treatment, health care, family, amusement and service industry Domain.
And people are promoted to anthropomorphic question and answer, independence for the requirement of the robot multiple mechanical action of substance of also conforming to the principle of simplicity And intelligent intelligent robot, the key factor that man-machine interaction also just develops as decision intelligent robot, therefore, improve intelligence The man-machine interaction efficiency of energy robot, the major issue as current urgent need to resolve, then corresponding, to the developer of robot Requirement higher is it is also proposed, to improve for handling machine people properties of product and robot data's treatment efficiency.
The content of the invention
One of technical problems to be solved by the invention are to need to provide a kind of raising for handling machine people's properties of product With robot data's treatment efficiency, the intelligent solution of intelligent robot is lifted.
In order to solve the above-mentioned technical problem, embodiments herein provide firstly a kind of text for intelligent robot Processing method, the intelligent robot is provided with robot operating system, and the method includes：Obtain pending text data； Core word extraction is carried out to the pending text data by syntactic analysis, being compared based on core word carries out Text normalization Treatment, wherein, the text data to comparing normalization failure based on core word carries out text normalizing based on Arithmetic of Semantic Similarity Change；Output and/or storage Text normalization result.
Preferably, in the step of carrying out Text normalization based on Arithmetic of Semantic Similarity, based on shortest path length and Depth capacity in classification carrys out computing semantic similarity.
Preferably, the Arithmetic of Semantic Similarity is to be mixed with to calculate similarity and the language according to concept according to semantic content Justice distance calculates the algorithm of similarity.
Preferably, in carrying out the step of Text normalization is processed based on core word comparison, by the textual data after normalization According to being placed in same normalization list, wherein, using the most short text data of number of words as the list normalization entry, it is other Text data as the list list member.
The embodiment of the present invention additionally provides a kind of text processing system for intelligent robot, the intelligent robot peace Equipped with robot operating system, text processing system includes：Text data acquisition module, it obtains pending textual data According to；Text normalization module, it passes through syntactic analysis carries out core word extraction to the pending text data, based on core Word is compared and carries out Text normalization treatment, wherein, the text data to comparing normalization failure based on core word, based on semantic phase Text normalization is carried out like degree algorithm；Result treatment module, its output and/or storage Text normalization result.
Preferably, the Text normalization module, it is further carrying out Text normalization based on Arithmetic of Semantic Similarity When, based on the depth capacity in shortest path length and classification come computing semantic similarity.
Preferably, the Arithmetic of Semantic Similarity is to be mixed with to calculate similarity and the language according to concept according to semantic content Justice distance calculates the algorithm of similarity.
Preferably, the Text normalization module, it is further carrying out Text normalization treatment based on core word comparison When, the text data after normalization is placed in same normalization list, wherein, using the most short text data of number of words as the row The normalization entry of table, other text datas as the list list member.
Compared with prior art, one or more embodiments in such scheme can have the following advantages that or beneficial effect Really：
The embodiment of the present invention carries out syntactic analysis and obtains each textual data by the pending text data to user input Core word in, being then based on core word comparison carries out Text normalization treatment to obtain Text normalization result.And, it is right The text data of normalization failure is compared based on core word, Text normalization is carried out based on Arithmetic of Semantic Similarity.The above method Intelligent robot and the interaction capabilities with user can be improved, preferable text can be carried out to pending text data Normalized, is easy to follow-up Language Processing, it is possible to increase the language interaction capabilities of intelligent robot, better meets user Demand.
Other features and advantages of the present invention will be illustrated in the following description, also, the partly change from specification Obtain it is clear that or being understood by implementing technical scheme.The purpose of the present invention and other advantages can by Specifically noted structure and/or flow are realized and obtained in specification, claims and accompanying drawing.
Brief description of the drawings
Accompanying drawing is used for providing to the technical scheme of the application or further understanding for prior art, and constitutes specification A part.Wherein, the accompanying drawing of expression the embodiment of the present application is used to explain the technical side of the application together with embodiments herein Case, but do not constitute the limitation to technical scheme.
Fig. 1 is the schematic flow sheet of the text handling method for intelligent robot of the application first embodiment.
The schematic diagram that Fig. 2 is processed for the Text normalization of the embodiment of the present application.
Fig. 3 is the structural representation of the text processing system for intelligent robot of the application second embodiment.
Specific embodiment
Describe embodiments of the present invention in detail below with reference to drawings and Examples, how the present invention is applied whereby Technological means solves technical problem, and reaches the implementation process of relevant art effect and can fully understand and implement according to this.This Shen Each feature that please be in embodiment and embodiment, can be combined with each other under the premise of not colliding, the technical scheme for being formed Within protection scope of the present invention.
In addition, the flow of accompanying drawing can be in the such as one group computer system of computer executable instructions the step of illustrating Middle execution.And, although show logical order in flow charts, but in some cases, can be with different from herein Order performs shown or described step.
In existing field in intelligent robotics, most of robots can carry out single interactive voice with user, complete Simple question and answer behavior is carried out into the task of user's imparting or with user.Existing this interactive mode be usually by with The dynamic question of householder, wakes up what robot was interacted therewith, and robot is looked into according to the problem of user from Q ＆ A database Corresponding response message is looked for, and voice messaging is exported according to response message.But, what existing Q ＆ A database requirement was inquired about asks Topic sentence possesses certain (aspect such as grammer, form) normalization, and user with robot when interactive voice is carried out, it is necessary to export Specification sex chromosome mosaicism, robot can just inquire corresponding result, and the influence of the factor such as individual character or age according to user, they The problem of proposition often has uncertainty, causes robot to inquire about corresponding answer from database according to problem.And And, it is this require user send specification sex chromosome mosaicism pattern also reduce user use robot interest, bring bad use Experience at family.The embodiment of the present invention proposes a kind of text handling method for intelligent robot, and the method can be asked similar Answer database to be updated, pending text data (such as substantial amounts of problem sentence) is carried out into Text normalization treatment, be Follow-up Language Processing (such as robot answers a question) lays the foundation.Specifically, this method passes through syntactic analysis to pending Text data carries out core word extraction, and being compared based on core word carries out Text normalization treatment, wherein, to being compared based on core word The text data of failure is normalized, Text normalization is carried out based on Arithmetic of Semantic Similarity, be finally completed normalized process, To improve for handling machine people properties of product and robot data's treatment efficiency.
First embodiment
Fig. 1 is the schematic flow sheet of the example one for being related to the text handling method for intelligent robot of the invention, should Intelligent robot is preferably the robot for being provided with robot operating system, however, other have the tables such as voice, expression, action Danone power, do not use the intelligent robot of the robot operating system (or equipment) the present embodiment can also be realized.Below Each step involved by illustrating the method with reference to Fig. 1.
In step s 110, pending text data is obtained.
During text message is obtained, substantial amounts of text message can be captured or by input equipment by network (such as keyboard, speech recognition apparatus etc.) directly enters pending text message.Pending text message can be a plurality of The text data of sentence, multiple vocabulary mixing compositions, such as including vocabulary " lawyer " and sentence " lawyer of immigrant's case defense " Text data.For follow-up Language Processing, user and the more preferable interactive voice of robot are realized, obtained in the present embodiment Pending text data is mainly the problem data of the multi-form being applied in dialogue, and these problem sentences are mostly with sentence Form statement, for example " Beethovan can be introduced ", the text such as " whom Beethovan is, I does not know, can tell me " Notebook data.
In the step s 120, core word extraction is carried out to pending text data by syntactic analysis, based on core word Comparison carries out Text normalization treatment.
Specifically, first the text message of different expression form in pending text data is sorted out, is obtained word The text message of species, the text message of sentence species.Because the text of word species itself has been entry form, because This need not again be processed to it.And for the text message of sentence species, in addition it is also necessary to will be directed to by syntactic analysis Core word is extracted, and thinks that Text normalization below is prepared.
When core word extraction is carried out, can be realized by the way of syntactic analysis.Specifically, it is exactly according to given Syntax rule collection, analyze the syntax rule employed in sentence forming process.The result of syntactic analysis is typically expressed as tree Structure, the node of tree represents the title of the syntactic units of sentence.Specific algorithm can be the interdependent syntactic analysis side based on conversion Method, in the analysis method based on conversion, dependency analysis are counted as performing some actions to input sentence, are built by these actions Erect contacting between word and word in sentence.Each action is all by current analysis State Transferring to new state.Based on conversion Analysis method do not search for the action sequence of global optimum, but use greedy strategy, select local according to current state Optimal action a, action would not change again once performing, thus also known as deterministic parsing method.
When being analyzed to " lawyer of immigrant's case defense ", output format is as follows：
0 immigrant's attribute of noun 1
The word structure of 1 case noun 3
The 2 defense complements of noun 1
3 attribute of structural auxiliary word 4
The core word of 4 lawyer's noun -1
It is thus determined that the core word of above-mentioned sentence is " lawyer ".
In addition to taking algorithm above, can also be using being integrated with probability context-free grammar, based on nerve net The interdependent syntactic analysis of network and the method for the interdependent syntactic analysis based on conversion.
After completing core word and extracting, being compared by core word carries out Text normalization treatment.In this example, by text The level of this normalized mainly includes：Word and word, word and sentence, sentence and sentence.In the mistake that core word is compared Cheng Zhong, if core word comparison result is consistent, the two is placed in same list, repeats this process, until by this Normalized content traversal is needed to complete.For convenience the later stage use the normalization list, normalize list in, choose number of words Minimum text data is used as normalization entry, other members as the normalization list.For example, " lawyer " and " immigrant The lawyer of case defense ", core word is " lawyer ", the two is placed in same class list by core word contrast, due to " rule The number of words of teacher " is minimum, then as normalization entry, and " lawyer of immigrant's case defense " used as the member of the list.
In step s 130, judge whether to compare the text data that normalization fails based on core word, if in the presence of, Step S140 is performed, step S150 is otherwise performed.
In step S140, the text data to comparing normalization failure based on core word, based on Arithmetic of Semantic Similarity Carry out Text normalization.
By step S120, the normalized of the most contents of pending text data is had been completed, however, Due to the difference between different sentences, it is likely that can there is the inconsistent situation of core word, in this case, lost for normalization The text data for losing, is normalized based on Arithmetic of Semantic Similarity.It should be noted that the Arithmetic of Semantic Similarity is It is mixed with the algorithm for similarity being calculated according to semantic content and similarity being calculated according to the semantic distance of concept, it is preferable that the language Adopted similarity algorithm is come computing semantic similarity based on the depth capacity in shortest path length and classification.
The Arithmetic of Semantic Similarity model such as following formula：
Wherein, the maxdepth (c) in denominator represents the depth capacity in WordNet classification tree.The algorithm considers two Concept c1, c2Between shortest path length length (c1,c2), for a fixed classification tree, between two concepts Path is bigger, and semantic similarity is smaller.For example the semantic similarity of " post-doctor " and " administrative personnel " be 0.57, " personage " and The semantic similarity of " post-doctor " is 0.4, then " post-doctor " and " administrative personnel " is divided into a class.
From from the perspective of information theory, the semantic similarity value that solution required by the algorithm is obtained is that path is provided between concept Information content.The calculating of the semantic similarity is not limited solely to the semantic depth in same classification, while improving all semantemes Connect the problem of equal weight.
Calculated by Arithmetic of Semantic Similarity above based on core word compare normalization failure text data in Similarity between core word, further carries out Text normalization treatment.
In step S150, export and/or storage Text normalization result.
When follow-up speech processes, treatment is analyzed using the normalized result, completes robot and user's Interactive voice.
In sum, the method for the present embodiment can improve intelligent robot and the interaction capabilities with user, treat The text data for the treatment of can carry out preferable Text normalization treatment, be easy to follow-up Language Processing, improve for processor Device people properties of product and robot data's treatment efficiency, so as to lift the language interaction capabilities of intelligent robot, better meet User's request.
Second embodiment
Fig. 3 is the structured flowchart of the text processing system 300 for intelligent robot of the embodiment of the present invention.Intelligence therein Energy machine is artificially provided with the robot of robot operating system.As shown in figure 3, the system 300 of the embodiment of the present application is mainly wrapped Include：Text data acquisition module 310, Text normalization module 320 and result treatment module 330.
Text data acquisition module 310, it obtains pending text data.
Text normalization module 320, it passes through syntactic analysis carries out core word extraction to the pending text data, Being compared based on core word carries out Text normalization treatment, wherein, the text data to comparing normalization failure based on core word, base Text normalization is carried out in Arithmetic of Semantic Similarity.Text normalization module 320, it is further based on Arithmetic of Semantic Similarity When carrying out Text normalization, based on the depth capacity in shortest path length and classification come computing semantic similarity.The semanteme Similarity algorithm is to be mixed with the algorithm for calculating similarity according to semantic content and similarity being calculated according to the semantic distance of concept. The Text normalization module 320, its further based on core word compare carry out Text normalization process when, will be pending Used as normalization entry, other text datas are normalized the most short text data of number of words as needs in text data List member.
Result treatment module 330, its output and/or storage Text normalization result.
The system 300 is configurable to the Text normalization model shown in Fig. 2.
By rationally setting, the system 300 of the present embodiment can perform each step of first embodiment, no longer go to live in the household of one's in-laws on getting married herein State.
Because the method for the present invention describes what is realized in computer systems.The computer system can for example be set In the control core processor of robot.For example, method described herein can be implemented as what can be performed with control logic Software, it is performed by the CPU in robot operating system.Function as herein described can be implemented as storage to be had in non-transitory Programmed instruction set in shape computer-readable medium.When implemented in this fashion, the computer program includes one group of instruction, When group instruction is run by computer, it promotes computer to perform the method that can implement above-mentioned functions.FPGA can be temporary When or be permanently mounted in non-transitory tangible computer computer-readable recording medium, for example ROM chip, computer storage, Disk or other storage mediums.In addition to being realized with software, logic as herein described can utilize discrete parts, integrated electricity What road and programmable logic device (such as, field programmable gate array (FPGA) or microprocessor) were used in combination programmable patrols Volume, or embodied including any other equipment that they are combined.All such embodiments are intended to fall under model of the invention Within enclosing.
It should be understood that disclosed embodiment of this invention is not limited to ad hoc structure disclosed herein, process step Or material, and the equivalent substitute of these features that those of ordinary skill in the related art are understood should be extended to.Should also manage Solution, term as used herein is only used for describing the purpose of specific embodiment, and is not intended to limit.
" one embodiment " or " embodiment " mentioned in specification means special characteristic, the structure for describing in conjunction with the embodiments Or characteristic is included at least one embodiment of the present invention.Therefore, the phrase " reality that specification various places throughout occurs Apply example " or " embodiment " same embodiment might not be referred both to.
While it is disclosed that implementation method as above, but described content is only to facilitate understanding the present invention and adopting Implementation method, is not limited to the present invention.Any those skilled in the art to which this invention pertains, are not departing from this On the premise of the disclosed spirit and scope of invention, any modification and change can be made in the formal and details implemented, But scope of patent protection of the invention, must be still defined by the scope of which is defined in the appended claims.

Claims (8)

1. a kind of text handling method for intelligent robot, the intelligent robot is provided with robot operating system, should Method includes：
Obtain pending text data；
Core word extraction is carried out to the pending text data by syntactic analysis, carrying out text based on core word comparison returns One change is processed, wherein, the text data to comparing normalization failure based on core word carries out text based on Arithmetic of Semantic Similarity Normalization；
Output and/or storage Text normalization result.
2. text handling method according to claim 1, it is characterised in that text is being carried out based on Arithmetic of Semantic Similarity In normalized step,
Based on the depth capacity in shortest path length and classification come computing semantic similarity.
3. text handling method according to claim 1 and 2, it is characterised in that
The Arithmetic of Semantic Similarity is to be mixed with calculate similarity according to semantic content and be calculated according to the semantic distance of concept The algorithm of similarity.
4. the text handling method according to any one of claims 1 to 3, it is characterised in that compared based on core word In carrying out the step of Text normalization is processed,
By the text data after normalization be placed on it is same normalization list in, wherein, using the most short text data of number of words as this The normalization entry of list, other text datas as the list list member.
5. a kind of text processing system for intelligent robot, the intelligent robot is provided with robot operating system, should Text processing system includes：
Text data acquisition module, it obtains pending text data；
Text normalization module, it passes through syntactic analysis carries out core word extraction to the pending text data, based on core Heart word is compared and carries out Text normalization treatment, wherein, the text data to comparing normalization failure based on core word, based on semanteme Similarity algorithm carries out Text normalization；
Result treatment module, its output and/or storage Text normalization result.
6. text processing system according to claim 5, it is characterised in that the Text normalization module, its is further When Text normalization is carried out based on Arithmetic of Semantic Similarity, calculated based on the depth capacity in shortest path length and classification Semantic similarity.
7. the text processing system according to claim 5 or 6, it is characterised in that
The Arithmetic of Semantic Similarity is to be mixed with calculate similarity according to semantic content and be calculated according to the semantic distance of concept The algorithm of similarity.
8. the text processing system according to any one of claim 5~7, it is characterised in that the Text normalization mould Block, it further when Text normalization treatment is carried out based on core word comparison, the text data after normalization is placed on same In normalization list, wherein, using the most short text data of number of words as the list normalization entry, other text datas make It is the list member of the list.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108920488A (en) * 2018-05-14 2018-11-30 平安科技（深圳）有限公司 The natural language processing method and device that multisystem combines
CN110889285A (en) * 2018-08-16 2020-03-17 阿里巴巴集团控股有限公司 Method, apparatus, device and medium for determining core word

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102937901A (en) * 2012-10-17 2013-02-20 武汉钢铁(集团)公司 Multi-tenant architecture design method
CN103020230A (en) * 2012-12-14 2013-04-03 中国科学院声学研究所 Semantic fuzzy matching method
CN106202159A (en) * 2016-06-23 2016-12-07 深圳追科技有限公司 A kind of man-machine interaction method of customer service system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102937901A (en) * 2012-10-17 2013-02-20 武汉钢铁(集团)公司 Multi-tenant architecture design method
CN103020230A (en) * 2012-12-14 2013-04-03 中国科学院声学研究所 Semantic fuzzy matching method
CN106202159A (en) * 2016-06-23 2016-12-07 深圳追科技有限公司 A kind of man-machine interaction method of customer service system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘小明 等: "一种结合本体和焦点的问题分类方法", 《北京理工大学学报》 *
董才正 等: "面向问答社区的中文问题分类", 《计算机应用》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108920488A (en) * 2018-05-14 2018-11-30 平安科技（深圳）有限公司 The natural language processing method and device that multisystem combines
CN110889285A (en) * 2018-08-16 2020-03-17 阿里巴巴集团控股有限公司 Method, apparatus, device and medium for determining core word
CN110889285B (en) * 2018-08-16 2023-06-16 阿里巴巴集团控股有限公司 Method, device, equipment and medium for determining core word

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