# Retrieval Verification: SemanticGraph2Vec_Semantic_graph_embedding_for_text_representation_parsed

## Pilar: Background
**Query:** _Penelitian ini dilatarbelakangi oleh urgensi masalah, konteks situasi saat ini, serta motivasi mendasar yang mendorong perlunya dilakukan kajian._

### Rank 1 (Score: 0.8239)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 2 (Score: 0.8152)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 3 (Score: 0.8148)
**Header:** **1. Introduction**
**Content:**
```text
The remaining of this paper is organized as follows. The main research objectives and contributions are outlined in Section 3. In Section 2, the related work on graph embedding are briefly reviewed. Section 4 describes the proposed model. Experimental results are discussed in Section 5. The conclusions are summarized in Section 6.
```

## Pilar: Methodology
**Query:** _Tahapan penelitian, arsitektur sistem, desain eksperimen, dan prosedur pengujian dieksekusi menggunakan metodologi teknis berikut._

### Rank 1 (Score: 0.8266)
**Header:** **1. Introduction**
**Content:**
```text
The remaining of this paper is organized as follows. The main research objectives and contributions are outlined in Section 3. In Section 2, the related work on graph embedding are briefly reviewed. Section 4 describes the proposed model. Experimental results are discussed in Section 5. The conclusions are summarized in Section 6.
```

### Rank 2 (Score: 0.8256)
**Header:** _5.3. Experiments_
**Content:**
```text
Experiments were performed to evaluate the ability of SemanticGraph2Vec to improve POS tagging tasks compare with other baseline graph embedding models. The evaluation metrics considered were precision, recall, and F-score. Precision is the proportion of correct decisions made over the total number of decisions for a given class. Recall refers to the fraction of correct decisions provided by the POS tagger over the total number of POS tags for a given class. The evaluation process is a conditional decision-making process in which the judgement of the proposed model is considered to be correct if and only if it is compatible with the ground truth labels in the data collection. Finally, the F-score is the harmonic mean of precision and recall.  
The sensitivity of the semantic walk length and total number of semantic walks for each vertex was measured and analyzed in separate experiments. Table 2 lists the accuracy results of applying the proposed model to various parameters.
```

### Rank 3 (Score: 0.8234)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

## Pilar: Gap
**Query:** _Apa kelemahan, keterbatasan ruang lingkup, atau masalah spesifik pada metode penelitian terdahulu yang belum terselesaikan?_

### Rank 1 (Score: 0.8482)
**Header:** **2. Related work**
**Content:**
```text
Over the past years, most proposed graph learning models have focused on preserving the structures of represented graphs, rather than on maintaining the semantic relationships between vertices. MetaGraph2Vec is the first model considering semantic relationships between vertices during the learning process. For semantic graph embedding, the MetaGraph2Vev model learns more informative embeddings by capturing rich semantic relationships between different types of vertices. It guides random walks in heterogeneous information networks to encode the semantic relationships between different types of vertices and generate heterogeneous walks through different types of vertices.  
It should be noted that there are many different datasets used for evaluating various methods and there is no common benchmark dataset that can be used for comparison. This makes it difficult and ambiguous to compare the proposed method to other methods. The majority of
```

### Rank 2 (Score: 0.8324)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 3 (Score: 0.8251)
**Header:** _4.1. Extract semantic relationships_
**Content:**
```text
In SemanticGraph2Vec, provided the semantic graph that represents the original text, semantic relationships are dynamically ranked by the frequency. In order to rank the semantic relations, the semantic graph is built on the basis of the dataset used, and the semantic relations are extracted and sorted according to their presence in the text. For example,
```

## Pilar: Objective
**Query:** _Apa tujuan utama, sasaran akhir, atau kontribusi spesifik yang dihasilkan oleh penelitian ini untuk menyelesaikan masalah tersebut?_

### Rank 1 (Score: 0.8267)
**Header:** **1. Introduction**
**Content:**
```text
The remaining of this paper is organized as follows. The main research objectives and contributions are outlined in Section 3. In Section 2, the related work on graph embedding are briefly reviewed. Section 4 describes the proposed model. Experimental results are discussed in Section 5. The conclusions are summarized in Section 6.
```

### Rank 2 (Score: 0.8260)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 3 (Score: 0.8245)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

## Pilar: Futurework
**Query:** _Apa saran, rekomendasi penelitian lanjutan, atau agenda di masa depan yang diusulkan oleh penulis untuk mengatasi keterbatasan studi saat ini?_

### Rank 1 (Score: 0.8291)
**Header:** **2. Related work**
**Content:**
```text
Over the past years, most proposed graph learning models have focused on preserving the structures of represented graphs, rather than on maintaining the semantic relationships between vertices. MetaGraph2Vec is the first model considering semantic relationships between vertices during the learning process. For semantic graph embedding, the MetaGraph2Vev model learns more informative embeddings by capturing rich semantic relationships between different types of vertices. It guides random walks in heterogeneous information networks to encode the semantic relationships between different types of vertices and generate heterogeneous walks through different types of vertices.  
It should be noted that there are many different datasets used for evaluating various methods and there is no common benchmark dataset that can be used for comparison. This makes it difficult and ambiguous to compare the proposed method to other methods. The majority of
```

### Rank 2 (Score: 0.8188)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 3 (Score: 0.8182)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```
