# An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Authors: Yanrong Gao
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Automatic recognition of prepositional phrases has always been pursued in English translation, and its accurate recognition is crucial for various activities and applications in the field of natural language processing. This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases. First, the method employs a simple noun phrase recognition model to identify and fuse phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases. Second, the method uses the CRF model to identify nested inner prepositional phrases; if there is nesting, it identifies the inner layer of nesting; if there is no nesting, it identifies prepositional phrases; finally, it fuses the inner prepositions identified in the initial corpus phrase, modifies its feature information, and retrains the foreign preposition phrase recognition model for recognition. After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, andF-value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of the other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases.


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## Body

## 1. Introduction

It has long been recognized as one of the most challenging problems in natural language processing, and it has received a great deal of attention and in-depth investigation both domestically and internationally. Numerous relevant studies have been conducted, with the most of them focusing on Chinese and English. However, the study of these two languages is highly distinct from one another due to the differences in their structural differences. It is common in English for the prepositional phrase (PP) to come at the conclusion of a sentence, which can easily lead to a syntactic structure misunderstanding over whether the PP modifies the preceding noun or the preceding verb. As a result, the study of prepositional phrase attachment in English places a high value on resolving the problem of PP attachment [1–5]. Many ways have been developed one after another, starting with the original rule-based approach and progressing to statistical methods, unsupervised and supervised learning, and the now common lexical vector representation, among others. There are still researches being conducted that are experimenting with various ways. The problem of preposition disambiguation is another issue that is closely related to this one. These two issues are syntactic ambiguity and semantic ambiguity, both of which are inextricably linked in their nature [6–10].It is possible to significantly minimize the difficulty of syntactic analysis, improve the performance of machine translation, and improve the effectiveness of information retrieval and text categorization with the right recognition of PP. As a result, PP recognition, as a component of natural language processing, is of critical importance to many people [11, 12].Scholars in the United States and overseas have carried out a variety of investigations and studies on the automatic delimitation of PP. The following are examples of representative approaches in English: rule-based conversion algorithm, heuristic unsupervised statistical algorithm, disambiguation algorithm based on syntactic and semantic analysis, and so on. These strategies are designed to target the word creation rules of the English PP language. These strategies are ineffective when it comes to Chinese PP recognition. Because of the complicated internal structure and ambiguous delimitation of Chinese PP, theF-value of the present recognition results is often in the 90 percent range [13–16]. In Chinese PP recognition, the methods utilized are based on shallow syntactic analysis, in which a model is used to identify the PP as a whole once word division and lexical annotation are completed. While some scholars proposed a method based on a ternary statistical model, which included first using collocation templates to obtain plausible collocation relations, then identifying PP based on plausible collocation relations, and finally using a combination of ternary statistical model and rules to identify PP not identified by plausible collocation relations, the ternary statistical model in this paper only considered three features [17–22], which were preposition, lexicality of the postboundary, and the presence of a preposition.As a result, some scholars proposed the method of partial phrase recognition based on SNP, which is a simple noun phrase with no complex modifying components inside, to first identify the SNPs in PP and fuse them together to simplify its internal structure and thus reduce the complexity of PP recognition; this method of recognition effect has been published so far and is a relatively good method of recognition effect [23, 24].As a result, some other researchers proposed an HMM model-based method of PP recognition that also included the use of dependency grammar for error correction. This was done because, because the internal structure of the PP is complex, a simple feature function cannot cover all of its characteristics and the HMM model cannot use complex features. However, some other researchers have applied the maximum fullness model to the PP recognition method and used the error definition method based on the dependency grammar to correct the recognition results. However, the maximum fullness model is unable to account for feature intensity, and the data sparsity problem is extremely severe in this method [25, 26]. Because PP is built of prepositions in conjunction with other entity phrases, performing entity phrase recognition on the corpus before recognizing PP can simplify the internal structure of PP, hence lowering the complexity of PP recognition. Following a review of previous studies, we discovered that the CRF model is the current model with the highest recognition in prepositional phrase recognition. However, the structural level of prepositional phrases has not been thoroughly investigated, and the research on prepositional nesting has not been sufficiently detailed to resolve the situation in which nested and parallel structures of prepositional phrases exist simultaneously [27–30]. This study offers an algorithm for automatically recognizing Chinese spatial prepositional phrases for academic literature, taking into account the fact that the majority of the phrases following prepositions in PP are made of noun phrase compositions. This model is used to identify the inner prepositional phrases in the test corpus, and after rule correction, the inner prepositional phrases in the initial corpus are fused and their annotation information is modified and retrained to obtain the nested prepositional phrases.The research contributions of the paper are as follows:(1)
This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases(2)
Adopt a simple noun phrase recognition model to identify and fuse the phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases(3)
After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, andF-value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases


## 2. Theoretical Underpinnings

### 2.1. The Introduction and the Issues of PP

The study of Chinese PP, in contrast to English, focuses on identifying PP in Chinese text as a whole after the division of words and lexical annotation, which generally falls under the category of shallow syntactic analysis and is also within the research scope of chunking. If the stringsWi,W2,⋯,Wj−1,Wj are PP to be recognized in the Chinese sentence S=Wi,W2,⋯,Wn−1,Wn,then Wi is a preposition, and the primary task of PP recognition is to identify Wi and Wj as the front and back boundaries of PP, respectively, and to identify the entire string. Moreover, while the preposition itself serves as the left boundary of the PP, which is straightforward to recognize, the essential challenge of identification is determining the location of the rear border.Automatic recognition of PP in Chinese is often challenging due to the features of the language itself, which include the following difficulties:(1)
The internal construction of PP is a difficult problem to solve. They are made up of a combination of prepositions and additional language components. It is possible for these components to be simple words (nouns, pronouns, and so on) or a variety of phrases (verb-object phrase, noun phrase, orientation phrase, time phrase, and so on), or they can even take the shape of a whole sentence. The underlying structure is complicated enough that it may readily develop a long-distance collocation relationship(2)
The presence of partitive prepositions in the sentence. Prepositions in Chinese can be employed as nouns, quantifiers, adjectives, conjunctions, and verbs, among other things. In order to ascertain the exact lexical character of a phrase, the context must sometimes be considered, which makes the identification of PP extremely complex(3)
PPs are frequently used in conjunction with one another or in complicated nested PP in the same sentence; for example, one big PP may include a number of smaller PP structures inside it. Because of this, determining the borders between the sentences becomes much more difficult(4)
Some PPs are inherently confusing in their construction. Many publications have examined ambiguous structures such as his view, which is ambiguous in nature. Occasionally, PPs that share the same structure cannot be correctly identified just based on internal information contained inside the sentence; instead, the contextual information must be employed to accurately identify PPs that have the same structure [30]


### 2.2. Sequence Annotation

CRFs can make full use of the contextual information features of words and is well suited for sequence annotation work; CRFs are learned from the training data in order to obtain the set of features and feature weights that maximize the conditional probability of the annotated sequence in the set of annotated sequences. Sequence analysis can effectively improve the processing efficiency of sentences and split and process the different structural components of sentences.Using the Chinese sentencesS=W1/P1W2/P2W3,⋯,Wi/Pi⋯Wn/Pn as an example; sequence annotation involves word separation and lexical annotation. Pi is the lexicality of the ith word, Wi is the ith word’s lexicality, and n is the number of the words [31].Simple noun phrase recognition makes use of the boundary stateBIO to designate the boundary state, where B signifies the left boundary of a simple noun phrase, I denotes the internal word or the right boundary, and O denotes the word that is not within the phrase (i.e., it is not inside the phrase). The purpose of the work is to obtain the labeled sequence T∗=T1,T2,⋯,Tn, such that the sequence has the highest probability among all possible labeled sequences, where T may be any of the input word sequences S=W1/P1W2/P2W3,⋯,Wi/Pi⋯Wn/Pn. And the letters Ti∈B,I,O represent the values.The goal of automatic PP recognition is to annotate all PP in a sentence without having to analyze the internal components of PP in the sentence. First, the sentenceS is divided into word1/pos1,word2/pos2⋯wordn/posn after word division and lexical annotation. wordi is the ith word; posn is the lexical property of the ith word. When the sequence has the highest probability among all potential annotation sequences, T∗=T1,T2,⋯,Tn is calculated so that the sequence has the highest probability among all possible annotation sequences, where Ti is one of the available values, which is Ti∈B,I,E,O. The letter B represents the initial word of the PP, the letter I represents the internal word of the PP, the letter E represents the final word of the PP, and the letter O represents the external word of the postpositional phrase. Furthermore, the output of the sequence includes all words that are not preceded by the letter O [32].


### 2.3. CRF

CRF is mostly used to solve problems with annotation. When compared to other sequence annotation models, CRF integrates spatial context features and generates global statistics, which can result in better annotation results. Additionally, CRF has been widely demonstrated to perform the function of lexical annotation, which has led to its widespread use in the field of natural language processing [33].CRF is a Markov random field for a random variableY given a random variable X. CRF is defined as follows.LetX=X1,X2,⋯Xn and Y=Y1,Y2,⋯Yn be sequences of random variables represented by linear chains, if the conditional probability distribution PY∣X of a sequence of random variables Y, given a sequence of random variables X, constitutes a linear chain conditional random field.
(1)PYi∣X,Y1,Y2,⋯,Yi,⋯,Yn=PYi∣X,Yi−1,Yi+1,where X denotes the input observation sequence and Yi denotes the corresponding output state sequence. The structure diagram of the linear CRF field is shown in Figure 1.Figure 1
Structure of linear CRF.On this basis, the probability for the condition thatX takes x and Yi takes the value y is
(2)Py∣x=1Zxexp∑i,kλktkyi−1,yi,x,i+∑i,lμlslyi,x,i,(3)Zx=∑yexp∑i,kλktkyi−1,yi,x,i−∑i,lμlslyi,x,i,where tk is the transfer feature; sl is the state feature; λk and μl are the weights corresponding to tk and sl, respectively; and Zx is the normalization factor. The conditional random field is completely determined by the feature functions tk and sl with the corresponding weights λk and μl.To unify the form oftk and sl and their weights λk and μl, assume that there are K1 transfer features and K2 state features, K=K1+K2, denoted as
(4)fkyi−1,yi,x,i=tkyi−1,yi,x,i,k=1,2,⋯,K1slyi,x,i,k=K1+l,l=1,2,⋯,K2.The summation of the transfer and state features at each positioni is denoted as
(5)fky,x,=∑i=1nfkyi−1,yi,x,i,k=1,2,⋯,K.Usingωk to denote the weights of the feature fky,x, then we have
(6)ωk=λk,k=1,2,⋯,K1μl,k=K1+l,l=1,2,⋯,K2.Then, we have(7)Py∣x=1Zxexp∑k=1Kωkfky,x,(8)Zx=∑yexp∑k=1Kωkfky,x.By usingωk and Fy,x, we then have
(9)Pωy∣x=expωFy,xZωx,(10)Zx=∑yexpωFy,x.


## 2.1. The Introduction and the Issues of PP

The study of Chinese PP, in contrast to English, focuses on identifying PP in Chinese text as a whole after the division of words and lexical annotation, which generally falls under the category of shallow syntactic analysis and is also within the research scope of chunking. If the stringsWi,W2,⋯,Wj−1,Wj are PP to be recognized in the Chinese sentence S=Wi,W2,⋯,Wn−1,Wn,then Wi is a preposition, and the primary task of PP recognition is to identify Wi and Wj as the front and back boundaries of PP, respectively, and to identify the entire string. Moreover, while the preposition itself serves as the left boundary of the PP, which is straightforward to recognize, the essential challenge of identification is determining the location of the rear border.Automatic recognition of PP in Chinese is often challenging due to the features of the language itself, which include the following difficulties:(1)
The internal construction of PP is a difficult problem to solve. They are made up of a combination of prepositions and additional language components. It is possible for these components to be simple words (nouns, pronouns, and so on) or a variety of phrases (verb-object phrase, noun phrase, orientation phrase, time phrase, and so on), or they can even take the shape of a whole sentence. The underlying structure is complicated enough that it may readily develop a long-distance collocation relationship(2)
The presence of partitive prepositions in the sentence. Prepositions in Chinese can be employed as nouns, quantifiers, adjectives, conjunctions, and verbs, among other things. In order to ascertain the exact lexical character of a phrase, the context must sometimes be considered, which makes the identification of PP extremely complex(3)
PPs are frequently used in conjunction with one another or in complicated nested PP in the same sentence; for example, one big PP may include a number of smaller PP structures inside it. Because of this, determining the borders between the sentences becomes much more difficult(4)
Some PPs are inherently confusing in their construction. Many publications have examined ambiguous structures such as his view, which is ambiguous in nature. Occasionally, PPs that share the same structure cannot be correctly identified just based on internal information contained inside the sentence; instead, the contextual information must be employed to accurately identify PPs that have the same structure [30]


## 2.2. Sequence Annotation

CRFs can make full use of the contextual information features of words and is well suited for sequence annotation work; CRFs are learned from the training data in order to obtain the set of features and feature weights that maximize the conditional probability of the annotated sequence in the set of annotated sequences. Sequence analysis can effectively improve the processing efficiency of sentences and split and process the different structural components of sentences.Using the Chinese sentencesS=W1/P1W2/P2W3,⋯,Wi/Pi⋯Wn/Pn as an example; sequence annotation involves word separation and lexical annotation. Pi is the lexicality of the ith word, Wi is the ith word’s lexicality, and n is the number of the words [31].Simple noun phrase recognition makes use of the boundary stateBIO to designate the boundary state, where B signifies the left boundary of a simple noun phrase, I denotes the internal word or the right boundary, and O denotes the word that is not within the phrase (i.e., it is not inside the phrase). The purpose of the work is to obtain the labeled sequence T∗=T1,T2,⋯,Tn, such that the sequence has the highest probability among all possible labeled sequences, where T may be any of the input word sequences S=W1/P1W2/P2W3,⋯,Wi/Pi⋯Wn/Pn. And the letters Ti∈B,I,O represent the values.The goal of automatic PP recognition is to annotate all PP in a sentence without having to analyze the internal components of PP in the sentence. First, the sentenceS is divided into word1/pos1,word2/pos2⋯wordn/posn after word division and lexical annotation. wordi is the ith word; posn is the lexical property of the ith word. When the sequence has the highest probability among all potential annotation sequences, T∗=T1,T2,⋯,Tn is calculated so that the sequence has the highest probability among all possible annotation sequences, where Ti is one of the available values, which is Ti∈B,I,E,O. The letter B represents the initial word of the PP, the letter I represents the internal word of the PP, the letter E represents the final word of the PP, and the letter O represents the external word of the postpositional phrase. Furthermore, the output of the sequence includes all words that are not preceded by the letter O [32].


## 2.3. CRF

CRF is mostly used to solve problems with annotation. When compared to other sequence annotation models, CRF integrates spatial context features and generates global statistics, which can result in better annotation results. Additionally, CRF has been widely demonstrated to perform the function of lexical annotation, which has led to its widespread use in the field of natural language processing [33].CRF is a Markov random field for a random variableY given a random variable X. CRF is defined as follows.LetX=X1,X2,⋯Xn and Y=Y1,Y2,⋯Yn be sequences of random variables represented by linear chains, if the conditional probability distribution PY∣X of a sequence of random variables Y, given a sequence of random variables X, constitutes a linear chain conditional random field.
(1)PYi∣X,Y1,Y2,⋯,Yi,⋯,Yn=PYi∣X,Yi−1,Yi+1,where X denotes the input observation sequence and Yi denotes the corresponding output state sequence. The structure diagram of the linear CRF field is shown in Figure 1.Figure 1
Structure of linear CRF.On this basis, the probability for the condition thatX takes x and Yi takes the value y is
(2)Py∣x=1Zxexp∑i,kλktkyi−1,yi,x,i+∑i,lμlslyi,x,i,(3)Zx=∑yexp∑i,kλktkyi−1,yi,x,i−∑i,lμlslyi,x,i,where tk is the transfer feature; sl is the state feature; λk and μl are the weights corresponding to tk and sl, respectively; and Zx is the normalization factor. The conditional random field is completely determined by the feature functions tk and sl with the corresponding weights λk and μl.To unify the form oftk and sl and their weights λk and μl, assume that there are K1 transfer features and K2 state features, K=K1+K2, denoted as
(4)fkyi−1,yi,x,i=tkyi−1,yi,x,i,k=1,2,⋯,K1slyi,x,i,k=K1+l,l=1,2,⋯,K2.The summation of the transfer and state features at each positioni is denoted as
(5)fky,x,=∑i=1nfkyi−1,yi,x,i,k=1,2,⋯,K.Usingωk to denote the weights of the feature fky,x, then we have
(6)ωk=λk,k=1,2,⋯,K1μl,k=K1+l,l=1,2,⋯,K2.Then, we have(7)Py∣x=1Zxexp∑k=1Kωkfky,x,(8)Zx=∑yexp∑k=1Kωkfky,x.By usingωk and Fy,x, we then have
(9)Pωy∣x=expωFy,xZωx,(10)Zx=∑yexpωFy,x.


## 3. The Proposed Method

For starters, an SNP recognition model was built using CRF, and then, the SNPs in the corpus were identified using the model, with the recognition results being corrected using the rule base in order to produce the SNP recognition results. A multilayer PP recognition model is developed using CRF once the corpus has been fused with the results of the SNP recognition. Finally, the PP recognition model is utilized to identify and recognize PPs, and the final results are produced by identifying the conversion rule set and rectifying the identified PPs using error-driven techniques and semantic analysis to obtain the final findings.


### 3.1. Participle Fusion

In this paper, we consider the SNP identification problem and PP identification problem as a sequence labeling problem; i.e., we identify SNPs and PPs by sequence labeling the test corpus with the CRF model. Firstly, the corpus is divided into words and lexical labels; i.e., the sentences are processed asS=word1/pos1,word2/pos2⋯wordn/posn format, where wordi is the ith word in the sentence, posi is the lexical nature of the first word, and n is the number of words contained in the sentence S. The goal is to obtain a corresponding annotated sequence T∗=T1,T2,⋯,Tn such that the sequence has the highest probability among all possible annotated sequences.The goal of CRF training is to find the optimalω value and then use the Viterbi algorithm to sequence label the unlabeled sequences. The task of sequence labeling is to find the Y, the largest possible labeled sequence that maximizes the conditional probability PωY∣X.
(11)Y∗=argmaxyPωY∣X.After identifying the SNPs in the corpus, the words were merged with the identified SNPs for word division and lexical annotation, and the fused SNPs were marked as “COM-NOUN.”For example, phrases are processed in friend’s home as shown in Table1.Table 1
SNP participle fusion table.
ClassContentInitial phraseIn friend’s homeLexical annotationIn/PREPOFriend/COM-NOUNB‘s/SUF-AFIHome/COM-NOUNBSequence annotationOBIOParticiple fusionIn/PREPOFriend’s//COM-NOUNBHome/COM-NOUNBThe PP template training section and the PP recognition module both make use of PP fusion. It is necessary to train the outer PP recognition template first, before moving on to the inner layer of the training corpus. The inner layer contains prepositional phrases, and their lexical features are identified with the letter PP. The inner PP layer of the test corpus is recognized by the PP recognition module, and if there is no nested PP in the corpus, the nested PP layer can be eliminated after recognition. Following recognition, the nested words can be eliminated.


### 3.2. Noun Phrase Recognition with a Simple Noun Phrase

The features used in this paper are word featureword and part-of-speech feature pos. The selected feature window size is 5. The feature template is shown in Table 2. The numbers in brackets indicate the position of the word. For example, word−1 indicates the current word. The front word of word0 represents the current word, and word1 represents the latter word of the current word.Table 2
SNP recognition feature template download original image.
NumberFeature descriptionFeature represent1Combination of any two adjacent words in the current word windowword0posiposi+12Combination of the current word with a word in the windowword0pos0posi3Lexical combination of three adjacent words in the windowposiposi+1posi+2The following rules are formulated based on the characteristics of noun phrases within PP, which can better correct the SNP recognition results and improve the recognition effect of PP significantly by not merging the back boundaries and postwords of PP to the maximum extent.(1)
If the antecedent of the identified SNP is an adverb of degree, the degree adverb modifies the first word of the SNP, and the first word is an adjective, the degree adverb is merged into the SNP(2)
If the phrase contains parallel components, the semantic similarity and word combination database methods are used to disambiguate the parallelism(3)
If the postboundary of the SNP is the name of the organization, the postboundary of the SNP is its predicate(4)
If the back-bounds of SNPs are adverbs such as all and all, the back-bounds of SNPs are the antecedents of the adverbs(5)
If the first two words of the SNP are nouns and the SNP is composed of three or more words, the front boundary of the SNP is the back boundary of the noun; otherwise, the marker is not a SNP(6)
If the postborder of the SNP is an indicative pronoun such as each, then the postborder of the SNP is its antecedentUsing atomic feature templates and composite feature templates, we employ the feature extraction approach described in literature [8] in this study, and we choose a feature window with a diameter of 5 pixels. Choose atomic feature templates, i.e., fundamental features, and select the following fundamental features:
(1)
Word featureword(2)
Phrase featurepos(3)
The candidate front boundary feature (CFB), which indicates whether or not there is a potential preposition before the word in question. The candidate preposition is marked as the preposition if it exists, and it is not marked if it does not. Then, mark it with the letterN(4)
In this case, whether or not the present word can be employed as the backbone of the prepositional phrase is considered a candidate backbone feature (CLB). In this research, we use a threshold value of 0.05 as a starting point. Thus, if the regency is larger than 0.05,U should indicate a Y, else N should be indicated for a feature(5)
If the current word can be employed as the word after the PP (CLW), then the current word is considered a candidate postword feature (CLW). Because of this, the threshold value for this paper is 0.05, which means that if the ratio is more than 0.05, thenU classifies the feature as Y, and otherwise, it is marked as N. The threshold value for this study is 0.05(6)
The word length feature, which indicates the length of the currently selected word. It is the collocation link between characteristics that is the emphasis of the composite template, which helps to increase the accuracy of PP recognitionThe fundamental concept of TBL is to modify labeling results in an error-driven manner, by identifying the conversion rules that provide the greatest amount of error correction based on predesigned conversion templates and target functions and then applying the rules that were generated to correct the labeling results. The aforementioned procedure is continued until no new rule is formed, which is comprised of trigger conditions and conversion rules, at which point the procedure is terminated. If the trigger condition is met during the result rectification process, the current result is adjusted by the conversion rule associated with the trigger condition.


## 3.1. Participle Fusion

In this paper, we consider the SNP identification problem and PP identification problem as a sequence labeling problem; i.e., we identify SNPs and PPs by sequence labeling the test corpus with the CRF model. Firstly, the corpus is divided into words and lexical labels; i.e., the sentences are processed asS=word1/pos1,word2/pos2⋯wordn/posn format, where wordi is the ith word in the sentence, posi is the lexical nature of the first word, and n is the number of words contained in the sentence S. The goal is to obtain a corresponding annotated sequence T∗=T1,T2,⋯,Tn such that the sequence has the highest probability among all possible annotated sequences.The goal of CRF training is to find the optimalω value and then use the Viterbi algorithm to sequence label the unlabeled sequences. The task of sequence labeling is to find the Y, the largest possible labeled sequence that maximizes the conditional probability PωY∣X.
(11)Y∗=argmaxyPωY∣X.After identifying the SNPs in the corpus, the words were merged with the identified SNPs for word division and lexical annotation, and the fused SNPs were marked as “COM-NOUN.”For example, phrases are processed in friend’s home as shown in Table1.Table 1
SNP participle fusion table.
ClassContentInitial phraseIn friend’s homeLexical annotationIn/PREPOFriend/COM-NOUNB‘s/SUF-AFIHome/COM-NOUNBSequence annotationOBIOParticiple fusionIn/PREPOFriend’s//COM-NOUNBHome/COM-NOUNBThe PP template training section and the PP recognition module both make use of PP fusion. It is necessary to train the outer PP recognition template first, before moving on to the inner layer of the training corpus. The inner layer contains prepositional phrases, and their lexical features are identified with the letter PP. The inner PP layer of the test corpus is recognized by the PP recognition module, and if there is no nested PP in the corpus, the nested PP layer can be eliminated after recognition. Following recognition, the nested words can be eliminated.


## 3.2. Noun Phrase Recognition with a Simple Noun Phrase

The features used in this paper are word featureword and part-of-speech feature pos. The selected feature window size is 5. The feature template is shown in Table 2. The numbers in brackets indicate the position of the word. For example, word−1 indicates the current word. The front word of word0 represents the current word, and word1 represents the latter word of the current word.Table 2
SNP recognition feature template download original image.
NumberFeature descriptionFeature represent1Combination of any two adjacent words in the current word windowword0posiposi+12Combination of the current word with a word in the windowword0pos0posi3Lexical combination of three adjacent words in the windowposiposi+1posi+2The following rules are formulated based on the characteristics of noun phrases within PP, which can better correct the SNP recognition results and improve the recognition effect of PP significantly by not merging the back boundaries and postwords of PP to the maximum extent.(1)
If the antecedent of the identified SNP is an adverb of degree, the degree adverb modifies the first word of the SNP, and the first word is an adjective, the degree adverb is merged into the SNP(2)
If the phrase contains parallel components, the semantic similarity and word combination database methods are used to disambiguate the parallelism(3)
If the postboundary of the SNP is the name of the organization, the postboundary of the SNP is its predicate(4)
If the back-bounds of SNPs are adverbs such as all and all, the back-bounds of SNPs are the antecedents of the adverbs(5)
If the first two words of the SNP are nouns and the SNP is composed of three or more words, the front boundary of the SNP is the back boundary of the noun; otherwise, the marker is not a SNP(6)
If the postborder of the SNP is an indicative pronoun such as each, then the postborder of the SNP is its antecedentUsing atomic feature templates and composite feature templates, we employ the feature extraction approach described in literature [8] in this study, and we choose a feature window with a diameter of 5 pixels. Choose atomic feature templates, i.e., fundamental features, and select the following fundamental features:
(1)
Word featureword(2)
Phrase featurepos(3)
The candidate front boundary feature (CFB), which indicates whether or not there is a potential preposition before the word in question. The candidate preposition is marked as the preposition if it exists, and it is not marked if it does not. Then, mark it with the letterN(4)
In this case, whether or not the present word can be employed as the backbone of the prepositional phrase is considered a candidate backbone feature (CLB). In this research, we use a threshold value of 0.05 as a starting point. Thus, if the regency is larger than 0.05,U should indicate a Y, else N should be indicated for a feature(5)
If the current word can be employed as the word after the PP (CLW), then the current word is considered a candidate postword feature (CLW). Because of this, the threshold value for this paper is 0.05, which means that if the ratio is more than 0.05, thenU classifies the feature as Y, and otherwise, it is marked as N. The threshold value for this study is 0.05(6)
The word length feature, which indicates the length of the currently selected word. It is the collocation link between characteristics that is the emphasis of the composite template, which helps to increase the accuracy of PP recognitionThe fundamental concept of TBL is to modify labeling results in an error-driven manner, by identifying the conversion rules that provide the greatest amount of error correction based on predesigned conversion templates and target functions and then applying the rules that were generated to correct the labeling results. The aforementioned procedure is continued until no new rule is formed, which is comprised of trigger conditions and conversion rules, at which point the procedure is terminated. If the trigger condition is met during the result rectification process, the current result is adjusted by the conversion rule associated with the trigger condition.


## 4. Experiment Results

Using the experimental corpus used in this paper is a corpus of academic writings from the previous ten years that has been divided into words and lexical annotations using the word division tool and presented in this paper. Specifically, the training corpus was structured to be acceptable for CRF training, whereas the test corpus was formatted by deleting phrases that did not contain PP and then using CRF to annotate the sentences that did contain PP. A total of six equal pieces make up the corpus, which are denoted by the numbers 1 through 6. These are known as corpuses 1, 2, 3, 4, 5, and 6. The technique of tenfold cross-validation is utilized, in which three of the corpora are used as training corpora and the other as test corpora, and four experiments are carried out. According to this research, the final recognition result is calculated by taking the average of the six experimental outcomes.Four comparison studies are carried out in this research for the purpose of PP recognition. Experiment 1 shows the results of PP recognition on the test corpus using the PP recognition model; Experiment 2 shows the results of PP recognition on the test corpus using the PP recognition model after the fusion of SNP recognition and then PP recognition on the test corpus using the PP recognition model after the fusion of SNP recognition and then PP recognition on the test corpus. When the experimental result of Experiment 1 is processed using the rule base, the resulting experimental result is known as Experiment 3. When the experimental result of Experiment 2 is subjected to rule processing, the resulting experimental result is known as Experiment 4.Figure2 depicts the outcomes of the experiments.Figure 2
Test results of PP.When compared to Experiment 1, the precision rate, recall rate, andF-value of Experiment 2 increased by 0.86 percent, 0.56 percent, and 0.72 percent, respectively, showing that the effect of PP recognition after adding simple noun phrase recognition was significantly improved; when compared to Experiment 1, the precision rate, recall rate, and F-value of Experiment 3 and Experiment 4 increased by 0.37 percentage points and 0.49 percentage points, respectively, showing the effect of rules after adding simple noun phrases. After the third and fourth experiments, the F-values increased by 0.37 percentage points and 0.49 percentage points, respectively.When the final results of this algorithm are compared to literature [11], literature [13], literature [14], and literature [15], it can be seen that the automatic recognition algorithm of Chinese spatial prepositional phrases for academic texts proposed in this paper achieves the best results for each index in Figures 3–5, respectively.Figure 3
Comparison of different methods with accuracy.Figure 4
Comparison of different methods with recall.Figure 5
Comparison of different methods with F1.


## 5. Conclusion

This paper provides a procedural discussion of prepositional analysis of sentences. Prepositional phrases are widely distributed in Chinese, and their correct recognition is important for many tasks and applications in the field of natural language processing. In order to alleviate the phenomenon of prepositional phrase nesting and juxtaposition, an automatic recognition algorithm of spatial prepositional phrases in Chinese academic texts is proposed. In this paper, we first extract simple noun phrases from the corpus, then fuse them into a single noun and tokenize them. Furthermore, prepositional phrases are recognized by the CRF model. Experimental results show that the method outperforms other algorithms in various metrics.



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*Source: 1797982-2022-03-23.xml*