Semantic Similarity Calculation Research Articles

Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms

Online network education offers convenience, however, the inefficiency and time-consuming nature of question-answering models negatively impact the demand for online learning. To address this issue, the study puts forward the development of an automatic English question-answering model. The improved model leverages a term frequence-inverse document frequency approach and an unsupervised participle algorithm based on deep learning. The precision and promptness of the question-answering model were enhanced by refining the weighted allocation of the term frequence-inverse document frequency algorithm and the unsupervised word-splitting algorithm. The validation shows that the improved precision rate is 68.14%, which is 34.37% and 50.45% more than the other two methods, respectively. The precision rate, recall rate, and F1 value for semantic similarity calculation improved by 9.23%, 9.22%, and 9.71%, respectively, compared to the traditional method. The validation experiments of the automatic English question-answering model indicate that its average accuracy was 94.68%, surpassing other models by 4.77%. The average answer time for the four types of questions was 30.52 ms, and the average answer time for the cause questions was 11.45 ms. The results show that the proposed English automatic question-answering model has better accuracy and timeliness of answering questions, and the improved accuracy for weight calculation is better. The English automatic question-answering model integrating word frequency-inverse document frequency and participle algorithm can satisfy the basic needs of teachers and students in online teaching, course question-answering, etc., which is of positive significance for the development of online education in the context of the Internet.

A Study of Business English Translation Skills Based on Parallel Corpus

Abstract In this paper, the support vector machine is used as the overall classification of the paragraph alignment method. The paragraph length relationship is taken as an essential feature of the business English paragraph alignment model, the CRFs discriminative model is used as the word alignment task model, and the maximum likelihood estimation algorithm is used for model parameter training. Aiming at the real-time alignment needs at the sentence or paragraph level in Business English, the word alignment algorithm based on semantic similarity calculation is proposed, and the evaluation indexes of English-Chinese EC, Chinese-English CE unidirectional alignment and bi-directional fusion alignment, and semantic extension alignment are compared. Adding traditional business English translation techniques, comparing the effects of conventional translation techniques on business English sentence and paragraph translation versus parallel corpus-based translation techniques. The dynamic chunking accuracy of two-way fusion alignment and semantic expansion alignment is 94.57% and 94.32%, respectively, and the accuracy of semantic expansion alignment is 6.85% and 8.17% higher than that of EC one-way alignment. Meanwhile, the number of errors in business English translation works based on parallel corpora is reduced, which can help translators overcome the interference of Chinese chapter structures and improve the quality of their translation works.

Stylistic Conversion and Linguistic Beauty Reproduction in English Translation-Based on Neurosemantic Analysis

Abstract The core of translation is language conversion, but it should be more of an aesthetic experience, a transformation of different ways of thinking. This paper proposes a neural semantic analysis method for English translation based on text similarity calculation. The technique uses a large-scale corpus to train the vector space model of words, establishes a concept-context matrix through the term-document matrix, and determines each value of the concept vector with a weight according to the literary translation in the knowledge base. The information extraction and evaluation of the English-translated text through semantic similarity calculation provides a theoretical basis for the subsequent analysis of style conversion and linguistic, and aesthetic reproduction of the English-translated text. The study will take the Lin Yutang translation and the Ricardian translation of The Analects of Confucius as an example to develop the analysis of style transformation and reproduction of linguistic beauty of the English-translated text from both lexical and syntactic levels. The results show that the lexical density of Lin Yutang’s translation is 0.5866, while that of Rie Jacob’s translation is 0.5334, with the former significantly larger than the latter. It shows that Lin Yutang’s translation of The Analects of Confucius adopts a naturalization strategy, i.e., To disseminate Chinese culture in a way that is authentic, familiar to English readers, and easy to understand, so as to make his linguistic expression more emotional.

Classification and Value Assessment of Sports Intangible Cultural Heritage Resources Combined with Digital Technology

Abstract This paper is dedicated to designing and constructing a knowledge ontology framework for sports intangible cultural heritage (ICH) resources, aiming to support their preservation and inheritance by integrating and mining sports ICH resources. The study includes the collection of multiple data of sports ICH from various data sources, and constructing ICH knowledge ontology using CIDOC CRM metadata reference model and seven-step method. To enrich the content of the ontology, the TextRank algorithm is used to extract textual critical information and design a domain-specific NER model for sports NRL. In addition, the study adopts the VSM vector space model for text representation and uses an improved hierarchical classification model for text categorization to improve classification accuracy. The study also explores the similarity calculation of concepts in the sports NRM ontology and proposes a semantic similarity calculation formula based on the ontology concepts. Respondents’ willingness to pay was investigated through the conditional value method (CVM) to assess the value of sports NRM tourism resources. Finally, the influencing factors of respondents’ willingness to pay were analyzed using statistical analysis and Logistic regression model, and it was found that they were mainly influenced by the degree of understanding of sports non-heritage resources and the level of education. The results of this study not only provide theoretical and methodological support for the effective integration and excavation of sports non-heritage resources and a new perspective for their protection, inheritance and sustainable development.

Analysis of Artificial Intelligence-Assisted Japanese Literature Teaching Reform and the Cultivation of Intercultural Communication Skills

Abstract With the deepening of Sino-Japanese economic and trade cooperation, the demand for enterprises for Japanese intercultural communication talents is increasing. In this paper, with the assistance of artificial intelligence, we constructed an innovative teaching mode of automatic association of Japanese expressions based on natural language processing technology and semantic analysis model of Japanese Literature, which deepens students’ understanding, memorization, and application of Japanese vocabulary by dispersing and associating the cultural context of Japanese Literature. The semantic similarity calculation parses participles and morphemes in Literature to find the vocabulary that can be substituted for the specified objects and form new example sentences. Finally, through the specific application of the innovative teaching mode by the Japanese language program of University X, the impact of the mode on the cultivation of students’ intercultural communicative competence was explored. The results show that the before-and-after difference in ICC competence in the class taught with the innovative mode is -10.134, while the before-and-after difference in the class prepared with the traditional mode is only -1.012, indicating that the proposed creative reform teaching mode has a pronounced effect on the cultivation of students’ intercultural communicative competence. This study realizes the digital reform of teaching the selected Japanese literature course and provides a reference path for cultivating students’ intercultural communicative competence.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Abstract Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

Multi-Modal Hashing for Efficient Multimedia Retrieval: A Survey

With the explosive growth of multimedia contents, multimedia retrieval is facing unprecedented challenges on both storage cost and retrieval speed. Hashing technique can project the high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. In the light of this, multi-modal hashing has recently received considerable attention to support large-scale multimedia retrieval. Different from uni-modal hashing, the multi-modal hashing focuses on modeling the multi-modal semantics and further preserving them into binary hash codes with hash learning. In this paper, we first systematically review the existing learning to hash methods for efficient multimedia retrieval, categorizing them according to the multimedia retrieval tasks, the specific multi-modal semantic modeling techniques, and hash learning strategies. Thereafter, we present the performance comparison results. We ultimately discuss the challenges and potential research directions that may require further investigation in multi-modal hash learning. To facilitate the research on multi-modal hashing, we develop an open-source performance comparison tool at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BMC-SDNU/Hashing-Retrieval</uri> .

Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Abstract This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in colleges and universities, puts forward a policy of informatization of foreign language teaching, and summarizes the ways in which college students’ foreign language learning behavior in the era of big data. Secondly, on the basis of corpus technology, the word vectorization representation of foreign language utterances is carried out, followed by similarity calculation of the word vectorization representation, judging the type of foreign language learning according to the results of foreign language semantic similarity calculation, and calculating the maximum weight path of the word vector sequence by using dynamic planning algorithm. Then, according to the demand analysis of the foreign language teaching corpus, the foreign language teaching corpus is constructed, and the application analysis of the corpus of foreign language teaching is carried out. The results indicate that the students in both classes have a similar understanding of the meaning and lexical properties of vocabulary. However, there is a certain gap in the collocation and utilization of vocabulary, and the corpus-based vocabulary teaching method is more conducive to students’ mastery of the target vocabulary than the traditional vocabulary teaching method, and the level of vocabulary learning is comparatively higher and more effective. The quality of foreign language teaching in colleges and universities can be improved by reference to this study.

Deep Semantic Understanding and Sequence Relevance Learning for Question Routing in Community Question Answering

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks.

Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.

A systematic knowledge graph-based smart management method for operations: A case study of standardized management

Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on.

Construction and Application of a Complex Network-Based Case Knowledge Base in an Assisted Instruction System

With the development of the business administration major, case teaching has become an effective teaching method. However, a lot of case resources exist in the business administration field, and the correlation between these cases is complex, which poses challenges to teachers and students in terms of information overload and knowledge organization. Existing case knowledge bases in assisted instruction systems often have shortcomings in terms of knowledge relationship strength calculation, text semantic similarity calculation, hierarchical knowledge clustering, and propagation evolution. To solve these problems, this study proposed a method of constructing and applying a case knowledge base in an assisted instruction system based on a complex network. This method mainly includes three aspects: calculation and reasoning of ontology relationship strength, calculation of ontology text semantic similarity, and hierarchical knowledge clustering and propagation evolution in complex networks. Through a comprehensive study of these three aspects, a more efficient and intelligent case knowledge base in an assisted instruction system was constructed, which not only improved the teaching efficiency and quality of the business administration major but also had the potential to promote teaching in other disciplines and fields. In addition, this study also provided new perspectives and methods in related fields, which is of great significance for efficiently organizing and utilizing knowledge.

Improving Efficiency and Accuracy in English Translation Learning: Investigating a Semantic Analysis Correction Algorithm

ABSTRACT Translation serves as a vital link in connecting individuals from diverse cultural backgrounds, assuming greater significance in the context of globalization. With the continued growth of international communication, the importance of effective translation in fostering mutual understanding, exploring new horizons, and building relationships cannot be overstated. However, the persistent language barrier necessitates improvements in translation accuracy and efficiency. In recent years, information technology has facilitated the development of innovative algorithms for English translation. This study aims to investigate correction algorithms for English translation learning, proposing a method that encompasses semantic word similarity calculations, a log-linear model construction, and the selection of appropriate translations using a dependency tree-to-string approach. Our research examines the performance of this system through comprehensive testing, showcasing noteworthy enhancements in the efficacy and precision of the translations in English. By addressing the users’ needs for English translation correction, this work contributes to bridging language gaps and fostering effective cross-cultural communication.

WordNet-based Computation of Semantic Similarity Between Two Vietnamese Nouns

Abstract. This study aimed to measure the semantic distance between Vietnamese nouns to determine their similarities and differences in terms of semantics. The study utilized a dataset of 1000 randomly selected noun pairs from the Vietnamese WordNet and applied different semantic distance measurement formulas, such as Path-length, Leacock &amp; Chodorow, Wu &amp; Palmer, Resnik, Lin, and Jiang-Conrath measures. The results showed that the Resnik measure is suitable for evaluating noun similarity among primitive concepts of the same class and for measuring proximity between two classes of primitive concepts. On the other hand, the Leacock &amp; Chodorow measure is appropriate for measuring the semantic distance between nouns belonging to two distant classes of primitive concepts. The methods used in this study have various potential applications in different fields of linguistics, such as determining synonyms, identifying semantically similar words, defining lexical-semantic fields, and detecting plagiarism.

Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system.

When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature. Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model. First of all, we calculated the similarity using edit distance to recall the candidate set of similar questions. Then, we took advantage of the BERT pretraining model to map the sentence information to the corresponding embedding representation. Finally, each dimensional feature of the sentence was obtained by passing a sentence vector through the multihead attention layer and the fully connected feedforward layer. The features that were stitched and fused were used for the semantic similarity calculation. Our improved model achieved a Top-1 precision of 0.551, Top-3 precision of 0.767, and Top-5 precision of 0.813 on 176 testing question sentences. In the analysis of the actual application effect of the model, we found that our model performed well in understanding the actual intention of users. Our deep learning model takes into account the background and classifications of questions and combines the efficiency of deep learning technology and the comprehensibility of semantics. The model enables the deep meaning of the user's question to be better understood by the intelligent question answering system, and answers that are more relevant to the original query are provided.

Clickbait Classification Model on Online News with Semantic Similarity Calculation Between News Title and Content

Clickbait is a sensational title that makes us click internet links to an article, image, or video. Online content providers use clickbait to gain user traffic, that leads to increasing income from the placed ads in their page. To attract more and more traffic, online content providers write sensational and hyperbolic titles, and even misleading and not telling the whole story. This can give us, the internet consumer, wrong perspective, and half-truth. And nowadays, clickbait titles are worse than ever. Modern clickbait titles are not hyperbolic nor ambiguous enough, and sometimes very hard to identify. This paper aims to classify clickbait titles, to help humans identify clickbait and stop sharing more online content that contains clickbait and misleading titles. This model classifies clickbait by calculating semantic similarity between the article title and the summary of the article content. The article content is summarized by T5 (Text-to-text Transfer Transformer) model. IndoBERT is then used to calculate semantic similarity score between generated summary and the article title. The article title, content, summary, and semantic similarity score are used for clickbait classification with various algorithms. The result shows that by adding article content alongside article title in the classification process improves F1-score by 7% when classified with IndoBERT. In another future research, this model can be integrated with another application such as twitter or telegram bot to send us warning every time a user consumes online content with clickbait title. Thus, it can prevent online communities from sharing misleading information caused by clickbait

SRU-based Multi-angle Enhanced Network for Semantic Text Similarity Calculation of Big Data Language Model

As a fundamental problem of natural language processing (NLP), the calculation of semantic text similarity plays a crucial role in a variety of big data application situations. In the process of text similarity modeling, however, owing to the complexity and ambiguity of Chinese semantics, effectively capturing the semantic interaction characteristics of Chinese text only from a single angle is impossible. This study proposes a deep learning-based computational model for semantic text similarity called SRU-based multi-angle enhanced network (SMAEN). Specifically, the authors firstly combine character-grained embeddings and word-granularity embeddings obtained from the pre-trained model to represent text. The text is encoded using a bidirectional simple recurrent unit (Bi-SRU) network, and the local text similarity is represented using a soft-aligned attention technique. In addition, the authors integrate Bi-SRU with an improved convolutional neural network (CNN) for global similarity modeling to capture semantic, time, and spatial characteristics of short text interaction. Finally, they employ a pooling layer to aggregate the calculation results into a fixed-length vector and a multi-layer perceptual (MLP) classifier to make a determination. Experimental results on Chinese public datasets LCQMC and PAWS-X show that the proposed method fully captures semantic interaction features from multiple angles and achieves advanced performance. This method can produce better matching results and enhance the accuracy of large data analysis. It is applicable to numerous scenarios involving large data, such as information retrieval and recommendation systems.

Research on the Automatic Subject-Indexing Method of Academic Papers Based on Climate Change Domain Ontology

It is important to classify academic papers in a fine-grained manner to uncover deeper implicit themes and semantics in papers for better semantic retrieval, paper recommendation, research trend prediction, topic analysis, and a series of other functions. Based on the ontology of the climate change domain, this study used an unsupervised approach to combine two methods, syntactic structure and semantic modeling, to build a framework of subject-indexing techniques for academic papers in the climate change domain. The framework automatically indexes a set of conceptual terms as research topics from the domain ontology by inputting the titles, abstracts and keywords of the papers using natural language processing techniques such as syntactic dependencies, text similarity calculation, pre-trained language models, semantic similarity calculation, and weighting factors such as word frequency statistics and graph path calculation. Finally, we evaluated the proposed method using the gold standard of manually annotated articles and demonstrated significant improvements over the other five alternative methods in terms of precision, recall and F1-score. Overall, the method proposed in this study is able to identify the research topics of academic papers more accurately, and also provides useful references for the application of domain ontologies and unsupervised data annotation.

NewsIN: A News Summarizer and Analyzer

Abstract: A summary condenses a lengthy document by highlighting salient features. It helps the reader to understand completely just by reading a summary so that the reader can save time and also can decide whether to go through the entire document. Summaries should be shorter than the original article so make sure to select only pertinent information to include the article. The main goal of a newspaper article summary is, the readers to walk away with knowledge on what the newspaper article is all about without the need to read the entire article. This work proposes a news article summarization system which access information from various local online newspapers automatically and summarizes information using heterogeneous articles. To make ad-hoc keyword-based extraction of news articles, the system uses a tailor-made web crawler which crawls the websites for searching relevant articles. Computational Linguistic techniques mainly Triplet Extraction, Semantic Similarity calculation and OPTICS clustering with DBSCAN is used alongside a sentence selection heuristic to generate coherent and cogent summaries irrespective of the number of articles supplied to the engine. The performance evaluation is one using the ROUGE metric. The rapid progresses in digital data acquisition techniques have led to a huge volume of news data available in the news websites. Most of such digital news collections lack summaries. Due to that, online newspaper readers are overloaded with lengthy text documents. Also, it is a tedious task for human beings to generate an abstract for a news event manually since it requires a rigorous analysis of the news documents. An achievable solution tothis problem is condensing the digital news collections and taking out only the essence in the form of an automatically generated summary which allows readers to make effective decisions in less time. The graph based algorithms for text summarization have been proven to be very successful over other methods for producing multi document summaries. The summary generated fromknowledge graphs is more in line with human reading habits and possesses the logic of human reasoning. Due to the fast-growing need of retrieving information in abstract form, we are proposing a novel approach for abstractive news summarization using the knowledge graphs to fulfill the need of having more accurate automatic abstractive news summarization and analyzer

Wikipedia bi-linear link (WBLM) model: A new approach for measuring semantic similarity and relatedness between linguistic concepts using Wikipedia link structure

Wikipedia links its articles by manually defined semantic relations called the Wikipedia hyperlink (link) structure. The existing Wikipedia link-based semantic similarity (SS) and semantic relatedness (SR) computation models, such as Wikipedia one-way link (WOLM) model and Wikipedia two-way link (WTLM) model, do not assess the strengths of the relationships between a candidate concept and its links (out-links or in-links). These models treat all the links as equally important even though some links are semantically more influential than others and should be given more importance. This phenomenon reduces the accuracy of these models. This paper presents the Wikipedia bi-linear link (WBLM) model that extends the previously proposed WOLM and WTLM models. The WBLM model explores the Wikipedia link structure as a semantic graph and discovers the strongly (bi-linear links) and weakly (out-links or in-links) connected links of a candidate concept. It improves the link-based vector representations of concepts by assigning weights to their connected links according to the strengths of their semantic associations. The experimental results demonstrate that the proposed WBLM model significantly improves the SS and SR computation accuracy of the WOLM model (6.9%, 8%, 24%, 17.3%, 31.2%, 30.6%, 26.5%, and 35.4%) and WTLM model (1.2%, 3.9%, 7.1%, 9.9%, 11%, 6.3%, 12.7%, and 13%), in terms of linear correlations with human judgments on gold standard benchmarks, including MC30, RG65, WS203, SimLex, 353All, MTurk287, MTurk771, and MEN3000, respectively. Moreover, this research offers a deep insight into the Wikipedia link structure and provides an adequate base for understanding it as a semantic graph.