CN108491414A - A kind of online abstracting method of news content and system of fusion topic feature - Google Patents
A kind of online abstracting method of news content and system of fusion topic feature Download PDFInfo
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Abstract
The present invention provides a kind of online abstracting method of news content of fusion topic feature, and step includes：The html of news pages is converted to dom tree, by all text nodes being ranked sequentially according to preorder traversal；The text of each text node is segmented, stop words is removed, obtains keyword；Based on the keyword, the compatible class of local maxima is generated, whole topics that full page generates is obtained, calculates each topic weight feature；Topic feature comprising the weight feature and non-content characteristic are quantized into the form of evidence, Fusion Features are carried out using DS evidence theories, obtains the probability that text node is text；The probability is smoothed, uses Otsu algorithms to calculate so that the maximum segmentation threshold of inter-class variance, obtains the text node of high characteristic value and as body.The present invention also provides a kind of online extraction systems of news content of fusion topic feature.
Description
Technical field
The present invention relates to technical field of communication processing, and in particular to a kind of news content of fusion topic feature is online
Abstracting method and system.
Background technology
With the development of internet, internet becomes one of the main channel that people obtain news, there is a large amount of news daily
It is generated on the net.News content extraction is the necessary step of the applications such as being analyzed news information, managed, retrieve, public sentiment with
The related systems such as intelligence analysis are required for carrying out the extraction of news information.News content information extraction method in real time online at present
Mainly news content extraction is carried out using the label characteristics of nested text.CEPF (Journal of Software, 27 (3):It is 714-735) one
Method of the kind based on label route characteristic on dom tree, obtains all label nodes with text on dom tree, then to institute first
There are the ratio features such as node calculate node pathdepth, text size, text variance with text, then uses multiplication to merge special
Value indicative improves short text characteristic value using Gaussian smoothing, the text node of high characteristic value is finally obtained into row threshold division.Online
It is real-time calculating mean that extraction algorithm need not pre-process webpage, offline study need not be also carried out to webpage；
Abstracting method needs have higher robustness simultaneously, can there is preferable extraction effect in the high different web pages of isomerism.
However, existing online news content abstracting method in real time, there is no the topic for considering that news content illustrates,
Cause to be easy by typesetting and pattern noise text identification similar with text to be text, for example carries big section word and text side
The big recommendation text of difference, CEPF methods are easy such noise text identification to be text, so as to cause content extraction accuracy rate
It reduces.
Invention content
The purpose of the present invention is to provide a kind of online abstracting methods of news content and system of fusion topic feature, pass through
Increase topic feature, improve the recognition accuracy of body text, the long text noise without news topic is preferably excluded,
Online quick extraction suitable for various high isomerism news website contents.
In order to achieve the above objectives, the present invention adopts the following technical scheme that：
A kind of online abstracting method of news content of fusion topic feature, step include：
The html of news pages is converted to dom tree, by all text nodes being ranked sequentially according to preorder traversal；
The text of each text node is segmented, stop words is removed, obtains keyword；
Based on above-mentioned keyword, the compatible class of local maxima is generated, obtains whole topics that full page generates, is calculated each
Topic weight feature；
Topic feature comprising above-mentioned weight feature and non-content characteristic are quantized into the form of evidence, use DS evidences
Theory carries out Fusion Features, obtains the probability that text node is text；
Above-mentioned probability is smoothed, uses Otsu algorithms to calculate so that the maximum segmentation threshold of inter-class variance, is obtained
Obtain the text node of high characteristic value and as body.
Further, the processing after being segmented to text further includes root reduction, removal everyday words.
Further, keyword is obtained using textRank algorithms.
Further, the topic feature further includes text node and brotgher of node text variance, text size summation, section
Height of the point on dom tree.
Further, the non-content characteristic includes hyperlink accounting, text size, node depth.
Further, the compatible class of local maxima is generated using greedy algorithm.Compatible class is a kind of particular set, in the middle two-by-two
All there is compatibility relation (a kind of from antisymmetric relation) between keyword, when the compatible class cannot be covered by the compatible class of bigger,
These compatible classes are exactly the compatible class of local maxima, and the compatible class of these local maximas is exactly all topics in html.
Specially：It first has to find reflexive and symmetrical compatibility relation, compatibility relation is defined on text node by this method
Middle co-occurrence is after keyword more than twice.The compatible class of local maxima is defined as follows：It is the set of all keywords to enable U,For arbitrary x, y ∈ W, all there is compatibility relation xRy, then W is compatible class；If phase can be added to without any k ∈ U-W
Hold in class W, then W is the compatible class of local maxima.Algorithm is as follows：
The compatible class greedy algorithm of 1 local maxima of algorithm-topic generates；
Input：Keyword set K；
Output：The compatible class set C of local maxima；
C is the compatible class of all local maximas being collected into algorithm 1, and K is all keyword set；relate
(word) method returns to all keywords with current word with compatibility relation；Pair (relatewords, word) is indicated
To generate after compatible class it is remaining it is all there is the word of compatibility relation to match two-by-two with word, compatible class C is added.The algorithm only scans
Current key word is all merged into existing compatible class by primary all keywords, every time scanning, finally obtains all parts
Maximal tolerance class.
It further, will be in the compatible class of local maxima since topic has the difference of text topic and non-text topic
Weight of the summation of the co-occurrence number of any two keyword as topic calculates the following institute of formula of topic (topic) weight
Show：
Tw (topic)=∑ cooc (x, y) | x, y ∈ topic }
Wherein, tw (topic) is the weight of topic, and cooc (x, y) is the number of x and y co-occurrences, and topic is a part
Maximal tolerance class.
Further, it is the method for carrying out uncertain reasoning using DS evidence theories as fusion method, for not true
Fixed, unclear information provides very effective synthetic method, and step includes：
After the characteristic value of topic feature and non-content characteristic is normalized into the number between 0~1, the Θ on identification framework
={ news ,~news, uncertainty } carries out probability assignments；
When being characterized in belonging to body, such as topic feature for describing node text, possess weight it is bigger if
Topic is more likely to be text, then carries out probability assignments according to following formula：
Wherein, topic (text) is all topics that current text includes, and topics is the topic in entire html；
When being characterized in being not belonging to body, such as node depth, the more complicated text of pattern for describing node text
More may be noise, node depth is deeper, then carries out probability assignments according to following formula：
Wherein, depth () method is used for the depth of calculate node, and TextNodes is all text nodes in html；
Then the fusion of feature is carried out with DS evidence theories (formula is as follows), obtaining fusion, one's duty fits on text hereinafter
Probability,
Wherein,
Further, to the tactic all text node text probability of the first sequence
(news) it is smoothed with Gaussian smoothing, the closer text feature value of distance influences bigger.With reference to CETR
(Proceedings of the 19thinternational conference on World wide web,ACM(2010)
971-980), realizing that the gaussian in one-dimensional discrete characteristic value is smooth using a kind of.If r is the radius of sliding window, window
Mouth size is 2r+1, and gaussian kernel function is as follows：
By the k of above formulaiIt is obtained after standardization：
By Gaussian kernel ki' obtained with by the tactic text feature value T progress convolutional calculation of preorder traversal：
To the text probability of text node (i.e.：Characteristic value) carry out it is smooth after, can be by the text of some low characteristic values
This section point is promoted, and reduces the characteristic value of noise text.Then Otsu algorithms is used to carry out the threshold value point of text node
It cuts, the text identification for obtaining all high features is body matter.What Otsu algorithms calculated is maximum between-cluster variance, if current selection
Threshold value be t, w0 be characterized value be more than t topic proportion, w1 be characterized value less than t topic proportion, u0 is big
In the characteristic value average value of t, u1 is the characteristic value average value less than t, and global mean value μ, the object function of Threshold segmentation is such as
Shown in lower：
G=w0(u0-μ)2+w1(u1-μ)2
Wherein, global mean value：
μ=w0×u0+w1×u1
Object function g is bigger, and the effect of the threshold value t of segmentation is better.The characteristic value of this paper indicates that codomain exists using probability
The use of 0.01 is step size computation g in t it is the value on 0~1 between 0~1, takes and allow the maximum t of g as segmentation threshold.
A kind of online extraction system of news content of fusion topic feature, including memory and processor, the memory
Computer program is stored, described program is configured as being executed by the processor, and described program includes for executing the above method
In each step instruction.
The method of the present invention has the following advantages：
This method does not need configuration template and rule, calculates segmentation threshold automatically, automatically by text under online environment
Text extracts, and solves the problems, such as to need a large amount of artificial progress template configurations in news extraction process, and human configuration
The maintenance cost of extraction template is very high, and the variation of any page may all cause decimation rule to fail, in each of high isomerism
High extraction accuracy rate can be kept on website, saved a large amount of human cost.This method is quick with a kind of efficient mode
News topic is generated, news topic feature has been merged so that the characteristic value for not having the long text noise of news topic reduces, and carries
The high recognition accuracy of body text.This method can be extracted on the text of multilingual, in different language
The page on extracted, it is only necessary to replacing the algorithm of participle can be operated.
This method need not carry out off-line training, need not also do any pretreatment to webpage, as long as webpage is inputted
It can be extracted in real time online in method, method is efficiently simple, with conveniently, has higher practical value.Through taking out
Experimental verification is taken, the average identification recall rate of Chinese website is 94.69%, and accuracy rate 92.23%, F1 values are 93.44%；English
The average identification recall rate of literary website is 95.32%, accuracy rate 88.74%, and F1 values are 91.91%, significant effect.
Description of the drawings
Fig. 1 is a kind of online abstracting method flow chart of news content of fusion topic feature in embodiment 1.
Fig. 2 is to give birth to newsy flow chart using the compatible class algorithm of local maxima.
Fig. 3 is the form of expression schematic diagram of topic in the html pages.
Specific implementation mode
Features described above and advantage to enable the present invention are clearer and more comprehensible, special embodiment below, and institute's attached drawing is coordinated to make
Detailed description are as follows.
Embodiment 1
The present embodiment discloses a kind of online abstracting method of news content of fusion topic feature, and news is carried out in Chinese website
Content extraction, as shown in Figure 1 (solid box part is news content).Before Chinese website is extracted, needing will be in method
Segmenting method is configured to Chinese word segmentation and Chinese stop words dictionary.Steps are as follows for this method：
1) url of Chinese news website is imported.
2) it is dom tree to parse the html files in url.
3) text node on dom tree is ranked sequentially by first sequence.
4) text in text node is segmented, removes stop words, obtains keyword using textRank algorithms, so
The compatible class (as shown in Figure 2) of local maxima is generated by greedy algorithm single pass afterwards, words are generated using the compatible class of local maxima
Topic, calculates the weight feature of topic.
5) it is special that text node and brotgher of node text variance, the height of text size summation, node on dom tree etc. are calculated
Sign.
6) by above-mentioned whole features and non-content characteristic (including hyperlink accounting, text size, node depth etc.) standard
It is inserted in the form of evidence after change, is merged using DS evidence theories, the probability that text node is text is obtained.Specific steps packet
It includes：After the characteristic value of topic feature and non-content characteristic is normalized into the number between 0~1, on identification framework Θ=
{ news ,~news, uncertainty } carries out probability assignments；When being characterized in belonging to body for describing node text,
Such as topic feature, possess the bigger topic of weight and is more likely to be text；When being characterized in being not belonging to for describing node text
Body, such as node depth, the more complicated text of pattern more may be noise, and node depth is deeper；Respectively according to corresponding
Formula carries out probability assignments；Then the fusion of feature is carried out with DS evidence theories, obtaining fusion, one's duty fits on text hereinafter
Probability.News topic and noise topic are as shown in Figure 3.
7) probability that node is text is subjected to Gaussian smoothing.
8) Otsu algorithms are used to calculate so that the maximum segmentation threshold of inter-class variance, the node for obtaining high characteristic value are
Body.
It is new in the www.xinhuanet.com, People's Net, phoenix net, 163 news, Tencent's news, Sina according to the abstracting method of the present embodiment
It hears six Chinese news websites and carries out extraction experiment, the average identification recall rate of Chinese website is 94.69%, the standard averagely identified
It is 93.44% that true rate, which is 92.23%, F1 values,.
Embodiment 2
The present embodiment discloses a kind of online abstracting method of news content of fusion topic feature, and news is carried out in English website
Content extraction.The automatic decimation of English website needs segmenting method being replaced due to linguistic difference.Due to English
Text only needs to segment according to space, so not needing complicated segmenting method, but needs to configure one behind segmenting method
The algorithm of root reduction.Steps are as follows for this method：
1) url of English news website is imported.
2) it is dom tree to parse the html files in url.
3) text node on dom tree is ranked sequentially by first sequence.
4) text in text node is segmented according to space, root reduction removes stop words, uses textRank
Algorithm obtains keyword, then generates the compatible class of local maxima by greedy algorithm single pass, uses the compatible class of local maxima
Topic is generated, the weight feature of topic is calculated.
5) it is special that text node and brotgher of node text variance, the height of text size summation, node on dom tree etc. are calculated
Sign.
6) by above-mentioned whole features and non-content characteristic (including hyperlink accounting, text size, node depth etc.) standard
It is inserted in the form of evidence after change, is synthesized using DS evidence theories, the probability that text node is text is obtained.
7) probability that node is text is subjected to Gaussian smoothing.
8) Otsu algorithms are used to calculate so that the maximum segmentation threshold of inter-class variance, the node for obtaining high characteristic value are news
Text.
According to the abstracting method of the present embodiment in CNN, BBC, NY Post, Yahoo！News, Freep, Nytimes six
English website carries out the extraction experiment of content, and it is 95.32% to obtain the recall rate averagely identified, and the accuracy rate averagely identified is
88.74%, F1 value are 91.91%.
The above embodiments are merely illustrative of the technical solutions of the present invention rather than is limited, the ordinary skill of this field
Personnel can be modified or replaced equivalently technical scheme of the present invention, without departing from the spirit and scope of the present invention, this
The protection domain of invention should be subject to described in claims.
Claims (10)
1. a kind of online abstracting method of news content of fusion topic feature, step include：
The html of news pages is converted to dom tree, by all text nodes being ranked sequentially according to preorder traversal；
The text of each text node is segmented, stop words is removed, obtains keyword；
Based on the keyword, the compatible class of local maxima is generated, whole topics that full page generates is obtained, calculates each topic
Weight feature；
Topic feature comprising the weight feature and non-content characteristic are quantized into the form of evidence, use DS evidence theories
Fusion Features are carried out, the probability that text node is text is obtained；
The probability is smoothed, uses Otsu algorithms to calculate so that the maximum segmentation threshold of inter-class variance, obtains high
The text node of characteristic value and as body.
2. according to the method described in claim 1, it is characterized in that, the processing after being segmented to text further include root also
Former, removal everyday words.
3. according to the method described in claim 1, it is characterized in that, obtaining keyword using textRank algorithms.
4. according to the method described in claim 1, it is characterized in that, the topic feature further includes text node and the brotgher of node
The height of text variance, text size summation, node on dom tree.
5. according to the method described in claim 1, it is characterized in that, the non-content characteristic includes that hyperlink accounting, text are long
Degree, node depth.
6. according to the method described in claim 1, it is characterized in that, generating the compatible class of local maxima using greedy algorithm.
7. according to the method described in claim 1, it is characterized in that, any two keyword in the compatible class of local maxima is total to
Weight of the summation of occurrence number as topic.
8. according to the method described in claim 1, it is characterized in that, by the topic feature comprising above-mentioned weight feature and it is non-in
Hold form of the characteristic quantification at evidence, step includes：
After the characteristic value of topic feature and non-content characteristic is normalized into the number between 0~1, on identification framework Θ=
{ news ,~news, uncertainty } carries out following probability assignments；
When being characterized in belonging to body for describing node text, then probability assignments are carried out according to following formula：
Wherein, topic (text) is all topics that current text includes, and topics is the topic in entire html；
When being characterized in being not belonging to body for describing node text, then probability assignments are carried out according to following formula：
Wherein, depth () method is used for the depth of calculate node, and TextNodes is all text nodes in html.
9. according to the method described in claim 1, it is characterized in that, carrying out Gaussian smoothing to the probability.
10. a kind of online extraction system of news content of fusion topic feature, including memory and processor, the memory are deposited
Computer program is stored up, described program is configured as being executed by the processor, and described program includes being wanted for executing aforesaid right
Ask the instruction of each step in any the methods of 1-9.
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Application publication date: 20180904 |