# Research on the Key Technology of Web Data Extraction and Mining Based on the Probability Distribution

Authors: Jinqiao Yang; Binghui Yang; Qi Sun; Shi Yan; Yuxin Miao
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In the development of internet technology innovation, the quantity of big data information continues to rise, showing the characteristics of dynamic, heterogeneous, massive, and so on. How to explore valuable and potential knowledge from the network system is the main topic of research and scholars. Based on the understanding of web data extraction and web mining technology, this paper makes a systematic study of web text mining, proposes the most commonly used hidden Markov model in probability distribution, and constructs the corresponding text mining method. The hidden Markov model is a statistical model, which is mainly used to represent a Markov process with unknown parameters. The difficulty in practical application lies in how to define the hidden parameters of the process from observable parameters and then use these parameters to conduct in-depth research. Finally, the practical results show that the hidden Markov model can not only obtain the information of different regions but also deeply analyze the data set, which proves the feasibility of this research technology.


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## Body

## 1. Introduction

In essence, the probability distribution refers to the probability law of the values of random variables. The probability of an event represents the probability of an outcome after the experiment. In order to fully understand the experiment, it is necessary to get all the possibilities and probabilities of the experiment, which is also regarded as the probability distribution of random experiment. Assuming that the results of an experiment are represented by the value of the variableX, the probability distribution of a random experiment can be regarded as the probability distribution of a random variable. Normal distribution is a key form of practical exploration, and variables in many biological phenomena obey or approach normal distribution, such as blood glucose content, milk yield, body weight, and hemoglobin content of livestock. Most statistical analysis methods are put forward with normal distribution as the core, so it is necessary to deeply understand and skillfully use relevant concepts in both theoretical research and practical application. Generally speaking, the normal distribution probability counting, wanting to combine standard normal distribution, is analyzed; the reason is that this form is the most simple, and all normal distribution can be reduced to a standard normal distribution is calculated; research of scholars has been combined with standard normal distribution function, compiled into normal distribution table query; this practice helps to simplify calculation steps.A research mainly from the perspective of the software database, this paper deeply explores the probability distribution as the core of data extraction and mining technology and therefore should not only master the related concepts of probability distribution, to clear the web information extraction technology and the steps of data mining technology, a comprehensive understanding of research scholars at home and abroad of web data extraction and mining technology research, in order to get more technical research experience. To extract mining data from software resource base, it is necessary to select specific technology to mine specific development process and finally assist developers to complete software engineering tasks according to the results.Data extraction is the basis of web data mining, which refers to a process of extracting measured data from embryonic text. Traditionally, information extraction uses natural language processing technology to process free files according to syntax or semantically limited extraction mode. Because web documents are semistructured text formats that contain a lot of markup and hyperlinks and few complete sentences, traditional natural language processing techniques do not meet the requirements of such information extraction. Web data extraction is regarded as a problem of extracting target information from web pages. Data should be extracted not only from natural language text but also from structured data of web pages. This paper focuses on the latter. Generally speaking, the structured data contained in the web refers to the relevant records obtained from background data, which will be presented in the web page according to a certain template. Extracting this kind of data can provide value-added services to users, such as shopping platforms, metasearch, and information collection, on the basis of integrating data from multiple web pages.In the research of web data mining, researchers mainly focus on two aspects, one is text clustering, and the other is automatic text classification. Among them, text clustering refers to one of the key technologies for unsupervised document organization, which first projects text into low-dimensional subspace and then uses clustering algorithm for mining and analysis. At present, the most common clustering methods include latent semantic index clustering, mixed model clustering, and spectral clustering. For example, Griffiths et al. proposed a potential Dirichlet distribution method with Gibbs sampling as the core in practical exploration, which does not require direct estimation of relevant parameters, but uses the Markov random process to simulate the distribution process of lexical topics and calculate the distribution of topics indirectly. At the same time, they also proposed the potential Dirichlet distribution method model and the dynamic potential Dirichlet distribution model, which are mainly used to deal with the problem of topic number selection and online evolution of topic clustering.Automatic text classification belongs to the basic condition of data retrieval and mining. In practice, it is necessary to judge the data category according to the text content on the basis of clearly marking the category set in advance. In the development of modern technology innovation, text classification is mainly used in information filtering, information management, natural language processing, and understanding. Generally speaking, automatic text classification consists of text representation, classification method, and classification effect evaluation, among which the vector space model is the most common representation method. In other words, words should be regarded as items and the frequency of items as weights. There are several kinds of typical methods of text classification such as the decision tree classification, Bayesian classification, and support vector machine (SVM) classification; the current research scholars mainly discuss the classification of support vector machine (SVM) method, not only reasonable applied to many fields but also obtained the excellent result in the practical development, such as the fuzzy theory and the maximum entropy model. It should be noted that, in the process of web data classification, the accuracy, equilibrium point, recall rate, accuracy rate, and other indicators should be used for evaluation and analysis, so as to clarify the effectiveness of the application algorithm model.With the increase of network information, the quantity of web text is more and more difficult to estimate, and because most web texts are unstructured or semistructured, the traditional data extraction and mining techniques are no longer in line with the requirements of web text mining. Therefore, researchers have conducted in-depth research on web text mining. Essentially, the web text mining is with its overlapping discipline, which involves the data mining, mathematical statistics, artificial intelligence, computer language, and other fields, although from a different point of view; the basic meaning of web text mining has a variety of understanding way, but the actual mining process is divided into the following main content, and concretes are shown in Figure1 [1–4].Figure 1
Flow chart of web text mining.First, in text collection and preprocessing, web crawler is used to collect web pages. After inputting the initial URL, text information of this web page is obtained, and related content is searched by combining the hyperlinks of web pages. Finally, data sets are constructed in repeated operations. In order to improve the quality of the data, you also need to preprocess the web text, such as scripts and file purging [5]Second, in feature representation and extraction, extract metadata representing features from web text, which can be stored in a structured form. Feature extraction is used to reduce the dimension of feature vectors [6].Third, in data mining, this operation is the extraction, classification, and prediction of specific information in a document file.Fourth, in mining evaluation, evaluate the knowledge model obtained by mining and provide the content conforming to the standards to the user [7].Fifth, in information presentation and navigation, visually process data mining results and provide users with information navigation to facilitate them to browse and obtain information [8].


## 2. Analysis of Web Data Extraction and Mining Technology Based on Probability Distribution

### 2.1. Probability Model

In order to study text mining process systematically, it is necessary to master the function of probability distribution in data analysis. When constructing the model, it is difficult to ensure the clarity of the final results by predictive analysis based on the input characteristics, and only the probability results closest to the truth with the model as the core can be obtained. Before analyzing the probability, it is necessary to clarify the preconditions for its existence: a random variable is an event that may or may not occur. If you lose that premise, then there is no value in studying probability distributions. Generally speaking, random variables are divided into two categories: discrete random variables and continuous random variables, which correspond to different probability distribution shapes, as shown in Figure2 [9].Figure 2
Content analysis of random variables.The defining properties of probability theory are as follows:Definition 1.
The functionPAcan be called A probability if the following conditions are met:
First, for all eventsA, PA≥0. Second, Pω=1, where ω represents a necessary event. Third, if multiple events A1,A2⋯An is not integrated with each other, then the following can be obtained:
(1)PA1+A2+⋯=PA1+PA2+.⋯Definition 2.
Assuming thatA and B represent two events that meet the condition of PB>0, PAB/PB can be regarded as the conditional probability of event A under the condition of event occurrence, from which PAB can be obtained.
In addition to the calculation of probability, it is assumed thatABC represents three events contained in the same sample space; then it can be obtained: first, PA+B=PA+PB−PAB. Second, PBC−PAC+PABC;PA+B+C=PA+PB+PC−PAB−, and finally, if multiple events A1,A2⋯An are not compatible, then we can get
(2)PA1+A2+⋯+An=PA1+PA2+⋯+PAn.
In the subtraction calculation of probability, letA and B represent two random events; then the following can be obtained:
(3)PB−A=PB−PAB.Theorem 3.
Under the condition ofB⊂A, we can get
(4)PB−A=PB−PA.
In the calculation of probability multiplication formula, it is assumed thatA1,A2,⋯,An represents N events and meets the condition of PA1,A2,⋯,An−1>0; then the following can be obtained:
(5)PA1,A2⋯An=PA1PA2A1PA3A1A2⋯PAnA1A2⋯An+1.
In the calculation and analysis of the full probability formula of probability, it is assumed thatA1,A2,⋯,An events constitute a complete set of events and meet the condition of PAi>0,i=1,2,⋯; then for all events B, the following can be obtained:
(6)PB=∑iPAiPBAi.Lemma 4.
In the probability Bayesian formula analysis, it is assumed thatA1,A2,⋯,An can constitute a complete set of events and meet the condition of PAi>0,i=1,2,⋯; then for all events B and PB>0, the following can be obtained:
(7)PAiB=−PAjPBAj∑iPAiPBAi,j=1,2,⋯.Corollary 5.
As a kind of statistical signal model, the hidden Markov model can use the obtained training samples for adaptive learning. In the late 1960s, Baum et al. proposed the basic theory of the model in empirical research. In the 1970s and 1980s, Baker et al. applied the model to the field of signal processing. It was not until 1990s that the model began to realize data extraction and mining combined with web text features. The specific definition is as follows [10]:
On the one hand, suppose thatXt,t∈T represents a random process and E represents the state space. If for any t1<t2<⋯<tn<t arbitrary x1,x2,⋯,xn,x∈E, the conditional distribution function of random variable Xt in the known condition Xt1=x1,Xt2=x2,⋯,Xtn=xn is only related to Xtn=xn and has no relationship with iXtn−1=xn−1,⋯,Xt2=x2, then the conditional distribution function meets the following requirements:
(8)Fx,txn,xn−1,⋯,x2,x1,tn,tn−1,⋯,t2,t1=Fx,txn,tn.
This process is known as a Markov process.Conjecture 6.
On the other hand, the random sequenceXn can be in state S1,S2,⋯,SN at any time, and the probability of its state qm+k at m+K is only related to its state Qm at m and has no relation to the state before M, so the following can be obtained [11]:
(9)PXm+k=qm+kXm=qm,Xm−1=qm−1,⋯,X1=q1=PXm+k=qm+kXm=qm.
In the above formula,Xn can be regarded as a Markov chain if the condition q1,q2,⋯,qm,qm+k∈S1,S2,⋯,SN is met.According to the structural diagram analysis of the Markov model as shown in Figure3, during data extraction and mining, the random process is regarded as a series of continuous transfer of states, and the state at time T is QT, which can be any result of N state sets [12].Figure 3
Structure diagram of the Markov model.Combined with the structural diagram analysis of the hidden Markov model, it is found in Figure4 [13].Figure 4
Structure diagram of hidden Markov model.


### 2.2. Problem Algorithm

Theorem 7.
First, evaluate the problem. This problem requires the analysis of a given modelλ, a sequence of observed values O=O1,O2,⋯,OT, and the specific study of probability POλ. There are two common algorithms:
On the one hand, the variables of the preceding algorithm are shown as follows:(10)αti=PO1,O2,⋯,Or,qt=siλ2≤t≤T.
The initialization result is(11)αli=πibiOll≤i≤N.
The recursive result is(12)αt+lj=∑i=1NαtiaijbjOt+l1≤t≤T−1,1≤j≤N,
Ends as follows:(13)POλ=∑i=lNαTi.
On the other hand, the variables of the latter algorithm are shown as follows:(14)βti=POt+1,Ot+2,⋯OTqt=si,λl≤t≤T−1,otherβTi=I.
The initialization result is(15)βTi=I1≤i≤N.
The recursive result is(16)βti=∑j=1NaijbjOt+1βt+ljt=T−1,T−2,⋯CI,≠i≤N.
End as follows:(17)POλ=∑i=1Nπibiolβli.
Second, in learning problems, the Baum-Welch algorithm should be used to deal with such problems. Whenξti,j represents the given training sequence O and model λ, the Markov chain is in state θi at time T and state θj at time t+L. The corresponding probability calculation formula is shown as follows:
(18)ξti,j=PO,qt=θi,qt+1=θjλ=αtiaijbjOt+1βt+lj/POλ.
The posterior probability function is defined as follows:(19)γti=pqt=sio,λ.
According to the definition, the relationship between them can be found as follows:(20)γti=∑j=1Nξli,j.Example 8.
By defining the above probability function and reevaluating the model parameters, the following results can be obtained:(21)π¯i=γli,a¯ij=∑t−1T−1ξti,j∑t−1T−1γti,b¯jk=t=lSZOt=Wk∑T−1γtj∑t=1T−1γtj.
Third, in the decoding problem, using the Viterbi algorithm to deal with the decoding problem, it is necessary to determine the best state sequenceQ∗=q1∗,q2∗,⋯,ql∗ on the basis of clearly observing the numerical sequence O=O1,O2,⋯,OT and model λ, where it is initialized as
(22)δIi=πbiiOI,1≤i≤NδIi=πbiiOI,1≤i≤N,ϕIi=0,1≤i≤NϕIi=0,1≤i≤N.
The recursive result is(23)δtj=max1≤i≤Nδt−1iaijbjOt,2≤t≤T,1≤j≤N,ϕtj=argmax1≤i≤Nδt−1iaij,2≤t≤T,1≤j≤N.
The final result is(24)P∗=max1≤i≤NδTi,qT∗=argmax1≤i≤NδTi.
The result of state sequence is(25)qt∗=ϕt+11≤i≤Nqt+1∗,t=T−1,T−2,⋯,l.


### 2.3. Framework Analysis

This paper studies the use of a website as a tool for web record extraction, and the overall process is divided into two stages: On the one hand, it is necessary to study the HTML tree formed by all the input web pages, including images, formats, and free text, and then use an entropy to evaluate the similarity of the HTML subtree to estimate the positioning data and divide the data into data segment sequence, which is the numerical content of the next stage. On the other hand, record templates that are expected to be presented are correctly populated, sorted sequentially. In this process, in order to complete the correct classification, two conditional models should be selected, one is MaxEnt and the other is MEMM, which can be used to train and observe different types of context information.This paper studies the use of the hidden Markov model to obtain specific information, combined with the training data set to build the corresponding model, and based on the model to complete the mining of specific information in the test data, the specific framework is shown in Figure5.Figure 5
Frame diagram of text data extraction and mining.First, in data extraction, this operation is the basic condition of data mining, which refers to the process of extracting specific data information from web text. Traditionally, information extraction takes advantage of natural language processing technology and regards syntax or semantics as constraints, so as to construct data extraction mode and process web text information freely. From a practical point of view, however, this technique has fallen short because of the semistructured nature of web text, which contains not only large numbers of tags and hyperlinks but also very few full sentences.Nowadays, the common extraction operation is divided into three forms: the first is the manual method, requiring programmers to observe the web page and source code based on the selection of mode to write programs to extract target data, and the selected mode is usually used to describe the location of tags. Secondly, it refers to wrapper induction, which belongs to a supervised learning method. It needs to first make use of manual annotation to identify the data records to be extracted and then regard the annotated web pages as learning samples, from which a group is extracted as learning rules, and then used to extract data information similar to web pages. Finally, it refers to automatic extraction, which belongs to unsupervised learning method. One or more web pages should be regarded as input content, and then mode or grammar should be automatically selected from them to complete information extraction. According to the structure diagram analysis shown in Figure6, it is found that the automatic extraction method is very suitable for processing large quantities of webpage information extraction, so it is the main topic of current scientific research.Figure 6
Automatically extracted structure diagram.Second, in data mining, web mining tasks are divided into three types: the first is content mining, the second is structure mining, and the last is using mining. From the perspective of information retrieval, the task of web text mining is to help users filter page information and improve the efficiency and quality of information retrieval. From the perspective of database, the task of Web text mining is to build and integrate data model and to support complex query, not simple keyword search. At present, the most common research on web text mining can be divided into two aspects. On the one hand, it refers to text clustering, and on the other hand, it refers to automatic text classification. The former, as the key content of unsupervised method to organize documents, should first project the text into low-dimensional subspace and then use clustering algorithm for processing. The latter, as the key content of information retrieval and text mining, needs to give a set of category tags and then judge the specific classification according to the text content.After obtaining record data, all records should be briefly processed to remove nonstandard content. Since my research is based on the text of marking training data for mining, word segmentation and word state should be marked for the recorded content. The specific record processing results are shown as follows:


### 2.4. Parameter Design

First,N=4 involves author, date, title, and affiliation and is represented by S1, S2, S3, and S4. Second, M represents the number of words in the text sequence; Thirdly, PII represents the probability of being in the Si state at the beginning of the experiment. Fourth, AIJ represents the specific probability of transition from state Si to state Sj. Fifth, bjk represents the actual probability of the word wk appearing at time t in the state.After the model structure is defined, the three parametersπI, AIj, and BJk are used for analysis. As the training data set studied in this paper has been marked, maximum likelihood can be directly used for evaluation in the analysis, as shown below:
(26)pi=Pqi=si=Thenumberoftimesthefirstwordinatextsequencebelongstostatesi∑i=14Thenumberofthefirstwordinatextsequencebelongstostatesi,aij=Pqt+1=sjqt=si=Numberoftransitionsfromstatesitostatesj∑j=1NNumberoftransitionsfromstatesitostatesj,bjk=POt=Wkqt=sj=ThenumberoftimesstatesjreleasesthewordwkThetotalnumberofoccurrencesofstatesjinatextsequence.If the training data set does not contain the words in the test data combination, then the zero probability problem will arise, which requires probability smoothing. There are many common smoothing methods, such as absolute discount method, linear reduction method, and absolute reduction method. On the basis of fully considering the web text sequence, the probability of the words before and after being in the same state is very large, so a relatively simple way is selected for processing during the experiment. If the probability of a word is zero, then use the probability of subsequent words to replace it. This way, the effect is better and the practical operation is easier.


## 2.1. Probability Model

In order to study text mining process systematically, it is necessary to master the function of probability distribution in data analysis. When constructing the model, it is difficult to ensure the clarity of the final results by predictive analysis based on the input characteristics, and only the probability results closest to the truth with the model as the core can be obtained. Before analyzing the probability, it is necessary to clarify the preconditions for its existence: a random variable is an event that may or may not occur. If you lose that premise, then there is no value in studying probability distributions. Generally speaking, random variables are divided into two categories: discrete random variables and continuous random variables, which correspond to different probability distribution shapes, as shown in Figure2 [9].Figure 2
Content analysis of random variables.The defining properties of probability theory are as follows:Definition 1.
The functionPAcan be called A probability if the following conditions are met:
First, for all eventsA, PA≥0. Second, Pω=1, where ω represents a necessary event. Third, if multiple events A1,A2⋯An is not integrated with each other, then the following can be obtained:
(1)PA1+A2+⋯=PA1+PA2+.⋯Definition 2.
Assuming thatA and B represent two events that meet the condition of PB>0, PAB/PB can be regarded as the conditional probability of event A under the condition of event occurrence, from which PAB can be obtained.
In addition to the calculation of probability, it is assumed thatABC represents three events contained in the same sample space; then it can be obtained: first, PA+B=PA+PB−PAB. Second, PBC−PAC+PABC;PA+B+C=PA+PB+PC−PAB−, and finally, if multiple events A1,A2⋯An are not compatible, then we can get
(2)PA1+A2+⋯+An=PA1+PA2+⋯+PAn.
In the subtraction calculation of probability, letA and B represent two random events; then the following can be obtained:
(3)PB−A=PB−PAB.Theorem 3.
Under the condition ofB⊂A, we can get
(4)PB−A=PB−PA.
In the calculation of probability multiplication formula, it is assumed thatA1,A2,⋯,An represents N events and meets the condition of PA1,A2,⋯,An−1>0; then the following can be obtained:
(5)PA1,A2⋯An=PA1PA2A1PA3A1A2⋯PAnA1A2⋯An+1.
In the calculation and analysis of the full probability formula of probability, it is assumed thatA1,A2,⋯,An events constitute a complete set of events and meet the condition of PAi>0,i=1,2,⋯; then for all events B, the following can be obtained:
(6)PB=∑iPAiPBAi.Lemma 4.
In the probability Bayesian formula analysis, it is assumed thatA1,A2,⋯,An can constitute a complete set of events and meet the condition of PAi>0,i=1,2,⋯; then for all events B and PB>0, the following can be obtained:
(7)PAiB=−PAjPBAj∑iPAiPBAi,j=1,2,⋯.Corollary 5.
As a kind of statistical signal model, the hidden Markov model can use the obtained training samples for adaptive learning. In the late 1960s, Baum et al. proposed the basic theory of the model in empirical research. In the 1970s and 1980s, Baker et al. applied the model to the field of signal processing. It was not until 1990s that the model began to realize data extraction and mining combined with web text features. The specific definition is as follows [10]:
On the one hand, suppose thatXt,t∈T represents a random process and E represents the state space. If for any t1<t2<⋯<tn<t arbitrary x1,x2,⋯,xn,x∈E, the conditional distribution function of random variable Xt in the known condition Xt1=x1,Xt2=x2,⋯,Xtn=xn is only related to Xtn=xn and has no relationship with iXtn−1=xn−1,⋯,Xt2=x2, then the conditional distribution function meets the following requirements:
(8)Fx,txn,xn−1,⋯,x2,x1,tn,tn−1,⋯,t2,t1=Fx,txn,tn.
This process is known as a Markov process.Conjecture 6.
On the other hand, the random sequenceXn can be in state S1,S2,⋯,SN at any time, and the probability of its state qm+k at m+K is only related to its state Qm at m and has no relation to the state before M, so the following can be obtained [11]:
(9)PXm+k=qm+kXm=qm,Xm−1=qm−1,⋯,X1=q1=PXm+k=qm+kXm=qm.
In the above formula,Xn can be regarded as a Markov chain if the condition q1,q2,⋯,qm,qm+k∈S1,S2,⋯,SN is met.According to the structural diagram analysis of the Markov model as shown in Figure3, during data extraction and mining, the random process is regarded as a series of continuous transfer of states, and the state at time T is QT, which can be any result of N state sets [12].Figure 3
Structure diagram of the Markov model.Combined with the structural diagram analysis of the hidden Markov model, it is found in Figure4 [13].Figure 4
Structure diagram of hidden Markov model.


## 2.2. Problem Algorithm

Theorem 7.
First, evaluate the problem. This problem requires the analysis of a given modelλ, a sequence of observed values O=O1,O2,⋯,OT, and the specific study of probability POλ. There are two common algorithms:
On the one hand, the variables of the preceding algorithm are shown as follows:(10)αti=PO1,O2,⋯,Or,qt=siλ2≤t≤T.
The initialization result is(11)αli=πibiOll≤i≤N.
The recursive result is(12)αt+lj=∑i=1NαtiaijbjOt+l1≤t≤T−1,1≤j≤N,
Ends as follows:(13)POλ=∑i=lNαTi.
On the other hand, the variables of the latter algorithm are shown as follows:(14)βti=POt+1,Ot+2,⋯OTqt=si,λl≤t≤T−1,otherβTi=I.
The initialization result is(15)βTi=I1≤i≤N.
The recursive result is(16)βti=∑j=1NaijbjOt+1βt+ljt=T−1,T−2,⋯CI,≠i≤N.
End as follows:(17)POλ=∑i=1Nπibiolβli.
Second, in learning problems, the Baum-Welch algorithm should be used to deal with such problems. Whenξti,j represents the given training sequence O and model λ, the Markov chain is in state θi at time T and state θj at time t+L. The corresponding probability calculation formula is shown as follows:
(18)ξti,j=PO,qt=θi,qt+1=θjλ=αtiaijbjOt+1βt+lj/POλ.
The posterior probability function is defined as follows:(19)γti=pqt=sio,λ.
According to the definition, the relationship between them can be found as follows:(20)γti=∑j=1Nξli,j.Example 8.
By defining the above probability function and reevaluating the model parameters, the following results can be obtained:(21)π¯i=γli,a¯ij=∑t−1T−1ξti,j∑t−1T−1γti,b¯jk=t=lSZOt=Wk∑T−1γtj∑t=1T−1γtj.
Third, in the decoding problem, using the Viterbi algorithm to deal with the decoding problem, it is necessary to determine the best state sequenceQ∗=q1∗,q2∗,⋯,ql∗ on the basis of clearly observing the numerical sequence O=O1,O2,⋯,OT and model λ, where it is initialized as
(22)δIi=πbiiOI,1≤i≤NδIi=πbiiOI,1≤i≤N,ϕIi=0,1≤i≤NϕIi=0,1≤i≤N.
The recursive result is(23)δtj=max1≤i≤Nδt−1iaijbjOt,2≤t≤T,1≤j≤N,ϕtj=argmax1≤i≤Nδt−1iaij,2≤t≤T,1≤j≤N.
The final result is(24)P∗=max1≤i≤NδTi,qT∗=argmax1≤i≤NδTi.
The result of state sequence is(25)qt∗=ϕt+11≤i≤Nqt+1∗,t=T−1,T−2,⋯,l.


## 2.3. Framework Analysis

This paper studies the use of a website as a tool for web record extraction, and the overall process is divided into two stages: On the one hand, it is necessary to study the HTML tree formed by all the input web pages, including images, formats, and free text, and then use an entropy to evaluate the similarity of the HTML subtree to estimate the positioning data and divide the data into data segment sequence, which is the numerical content of the next stage. On the other hand, record templates that are expected to be presented are correctly populated, sorted sequentially. In this process, in order to complete the correct classification, two conditional models should be selected, one is MaxEnt and the other is MEMM, which can be used to train and observe different types of context information.This paper studies the use of the hidden Markov model to obtain specific information, combined with the training data set to build the corresponding model, and based on the model to complete the mining of specific information in the test data, the specific framework is shown in Figure5.Figure 5
Frame diagram of text data extraction and mining.First, in data extraction, this operation is the basic condition of data mining, which refers to the process of extracting specific data information from web text. Traditionally, information extraction takes advantage of natural language processing technology and regards syntax or semantics as constraints, so as to construct data extraction mode and process web text information freely. From a practical point of view, however, this technique has fallen short because of the semistructured nature of web text, which contains not only large numbers of tags and hyperlinks but also very few full sentences.Nowadays, the common extraction operation is divided into three forms: the first is the manual method, requiring programmers to observe the web page and source code based on the selection of mode to write programs to extract target data, and the selected mode is usually used to describe the location of tags. Secondly, it refers to wrapper induction, which belongs to a supervised learning method. It needs to first make use of manual annotation to identify the data records to be extracted and then regard the annotated web pages as learning samples, from which a group is extracted as learning rules, and then used to extract data information similar to web pages. Finally, it refers to automatic extraction, which belongs to unsupervised learning method. One or more web pages should be regarded as input content, and then mode or grammar should be automatically selected from them to complete information extraction. According to the structure diagram analysis shown in Figure6, it is found that the automatic extraction method is very suitable for processing large quantities of webpage information extraction, so it is the main topic of current scientific research.Figure 6
Automatically extracted structure diagram.Second, in data mining, web mining tasks are divided into three types: the first is content mining, the second is structure mining, and the last is using mining. From the perspective of information retrieval, the task of web text mining is to help users filter page information and improve the efficiency and quality of information retrieval. From the perspective of database, the task of Web text mining is to build and integrate data model and to support complex query, not simple keyword search. At present, the most common research on web text mining can be divided into two aspects. On the one hand, it refers to text clustering, and on the other hand, it refers to automatic text classification. The former, as the key content of unsupervised method to organize documents, should first project the text into low-dimensional subspace and then use clustering algorithm for processing. The latter, as the key content of information retrieval and text mining, needs to give a set of category tags and then judge the specific classification according to the text content.After obtaining record data, all records should be briefly processed to remove nonstandard content. Since my research is based on the text of marking training data for mining, word segmentation and word state should be marked for the recorded content. The specific record processing results are shown as follows:


## 2.4. Parameter Design

First,N=4 involves author, date, title, and affiliation and is represented by S1, S2, S3, and S4. Second, M represents the number of words in the text sequence; Thirdly, PII represents the probability of being in the Si state at the beginning of the experiment. Fourth, AIJ represents the specific probability of transition from state Si to state Sj. Fifth, bjk represents the actual probability of the word wk appearing at time t in the state.After the model structure is defined, the three parametersπI, AIj, and BJk are used for analysis. As the training data set studied in this paper has been marked, maximum likelihood can be directly used for evaluation in the analysis, as shown below:
(26)pi=Pqi=si=Thenumberoftimesthefirstwordinatextsequencebelongstostatesi∑i=14Thenumberofthefirstwordinatextsequencebelongstostatesi,aij=Pqt+1=sjqt=si=Numberoftransitionsfromstatesitostatesj∑j=1NNumberoftransitionsfromstatesitostatesj,bjk=POt=Wkqt=sj=ThenumberoftimesstatesjreleasesthewordwkThetotalnumberofoccurrencesofstatesjinatextsequence.If the training data set does not contain the words in the test data combination, then the zero probability problem will arise, which requires probability smoothing. There are many common smoothing methods, such as absolute discount method, linear reduction method, and absolute reduction method. On the basis of fully considering the web text sequence, the probability of the words before and after being in the same state is very large, so a relatively simple way is selected for processing during the experiment. If the probability of a word is zero, then use the probability of subsequent words to replace it. This way, the effect is better and the practical operation is easier.


## 3. Experimental Analysis

### 3.1. Evaluation Criteria

According to the performance evaluation index of constant information extraction proposed in the conference in the early 1990s, this paper selects two items for analysis, one is recall rate (REC), and the other is accuracy rate (PRE). The specific calculation formula is as follows:(27)REC=ExtractthecorrectnumberofwordsmarkedinafieldintheresultTotalnumberofwordsinafieldinthesmaple=Correct‐MarkedWordsWordsinDataSet,REC=ExtractthecorrectnumberofwordsmarkedinafieldintheresultExtractthetotalnumberofwordsinafieldinthesturcture=Correct‐MarkedWordsWordsinExtractedResultSet.


### 3.2. Result Analysis

In this paper, the VC6.0 programming language and Windows environment were used for experimental operations. The selected experimental data contained the heads of 944 papers. Each time, part of the content was selected as the training data set, and the rest were the test data set. In the experimental analysis, the following results can be obtained: Title represents the title of a paper, author represents the author of a paper, date represents the publication time, and affiliation represents the affiliation.In the first experimental analysis, 600 paper heads were selected for analysis, including 500 training data sets containing 11,472 words in total and 100 test data sets containing 2,203 words in total. The actual analysis results are shown in Table1.Table 1
Experimental results of the first group.
The domain nameThe number of wordsTitleAuthorDateAffiliationCorrect-marked words74955785621Words in data set80662193683Words in extracted result set835575101691REC0.9292800.8969400.9139780.909224PRE0.8970010.9686960.8415820.898698In the second group, 830 paper heads were selected for analysis, including 600 training data sets, 13,909 words in total, and 230 test data sets, including 4,934 words in total. The actual research results are shown in Table2.Table 2
Experimental results of the second group.
The domain nameThe number of wordsTitleAuthorDateAffiliationCorrect-marked words170511821641356Words in data set181614251751518Words in extracted result set190712422321609REC0.9388770.8294730.9371430.893281PRE0.8940740.9516900.7068970.842759Compared with the above results, it is found that the recall rate and accuracy of web text data are very high. This proves that it is effective to mine marked data information by using the hidden Markov model and maximum likelihood extraction. Among them, title has the highest accuracy rate, with an average value of 0.934. Author had the highest recall rate, with an average value of 0.961. Under the condition that the amount of test and training data keeps increasing, the recall rate of DATE will continue to rise. The reason for this change is that there is not much data information in this area, and the corresponding recall rate will increase as the data information of test set keeps increasing.


## 3.1. Evaluation Criteria

According to the performance evaluation index of constant information extraction proposed in the conference in the early 1990s, this paper selects two items for analysis, one is recall rate (REC), and the other is accuracy rate (PRE). The specific calculation formula is as follows:(27)REC=ExtractthecorrectnumberofwordsmarkedinafieldintheresultTotalnumberofwordsinafieldinthesmaple=Correct‐MarkedWordsWordsinDataSet,REC=ExtractthecorrectnumberofwordsmarkedinafieldintheresultExtractthetotalnumberofwordsinafieldinthesturcture=Correct‐MarkedWordsWordsinExtractedResultSet.


## 3.2. Result Analysis

In this paper, the VC6.0 programming language and Windows environment were used for experimental operations. The selected experimental data contained the heads of 944 papers. Each time, part of the content was selected as the training data set, and the rest were the test data set. In the experimental analysis, the following results can be obtained: Title represents the title of a paper, author represents the author of a paper, date represents the publication time, and affiliation represents the affiliation.In the first experimental analysis, 600 paper heads were selected for analysis, including 500 training data sets containing 11,472 words in total and 100 test data sets containing 2,203 words in total. The actual analysis results are shown in Table1.Table 1
Experimental results of the first group.
The domain nameThe number of wordsTitleAuthorDateAffiliationCorrect-marked words74955785621Words in data set80662193683Words in extracted result set835575101691REC0.9292800.8969400.9139780.909224PRE0.8970010.9686960.8415820.898698In the second group, 830 paper heads were selected for analysis, including 600 training data sets, 13,909 words in total, and 230 test data sets, including 4,934 words in total. The actual research results are shown in Table2.Table 2
Experimental results of the second group.
The domain nameThe number of wordsTitleAuthorDateAffiliationCorrect-marked words170511821641356Words in data set181614251751518Words in extracted result set190712422321609REC0.9388770.8294730.9371430.893281PRE0.8940740.9516900.7068970.842759Compared with the above results, it is found that the recall rate and accuracy of web text data are very high. This proves that it is effective to mine marked data information by using the hidden Markov model and maximum likelihood extraction. Among them, title has the highest accuracy rate, with an average value of 0.934. Author had the highest recall rate, with an average value of 0.961. Under the condition that the amount of test and training data keeps increasing, the recall rate of DATE will continue to rise. The reason for this change is that there is not much data information in this area, and the corresponding recall rate will increase as the data information of test set keeps increasing.


## 4. Conclusion

When analyzing data extraction and mining technologies, researchers in various countries should constantly expand the search scope and fully integrate information data from different fields, so as to get rid of the limitation of traditional technology concepts and find better optimization algorithms. In view of this, this paper systematically understands the extraction and mining of web data, makes clear the importance of key technologies in practical operation, and combines hidden Markov model to carry out in-depth mining, so as to grasp more high-quality research results. Because the hidden Markov model has the advantages of high efficiency, mature algorithm, and in-depth research, the application of maximum likelihood method in web text mining is the main topic of current research and discussion. Combined with the research results of this paper, it is proved that hidden Markov model plays an active role in web text mining. At the same time, the training of professional and technical personnel should be strengthened, and the key technologies of web data extraction and mining should be explored from the perspective of sustainable development.



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*Source: 6714785-2022-06-11.xml*