Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes

Shirakawa, Masumi; Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro

doi:10.1109/tetc.2015.2418716

Cited by 44 publications

(20 citation statements)

References 26 publications

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“…The process of semantic classification implements the method of CTS using RTHM. The CTS method derives the mechanism of "Naïve Bayesian Classifier (NBC)" [9] to recognize the probable semantic relation. The CTS method initially as assumes that the extracted features terms are independent from a class due to it variance in values, which is defined as condition independency in NBC to make simplification in individual relation computation.…”

Section: Semantic Classification Using Rthmmentioning

confidence: 99%

“…It is logical to involve them in the classification process to support semantic applications by providing a domain-oriented visualization [6], [7]. But, with the growth of the semantic web and the existence of many tools to create and manage ontology, the ontology of different domains of knowledge is already available [8], [9]. Their participation in the discovery of knowledge based on automated learning has increased and promising results have been obtained.…”

Section: Introductionmentioning

confidence: 99%

“…The WIS is a fundamental problem in the recommendation and choice of service-oriented computing [9], [10]. Current search in the WIS and recommend either destruction of UDDI records, dominant keyword, or servicebased web service policies for QoS [11], [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Semantic based Terms Relation Research for Classification in Web Information Mining

Thota¹,

Mahalakshmi²

2019

IJRTE

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The rapid growth of web information and its services in different areas such as e-commerce, healthcare, digital marketing, online booking, etc. is a challenge in providing accurate information in the domain services related to the user's query. The current web information of services classifies the retrieval of the relevant service and assists the classification by supporting the knowledge and classifications of the specific service information. Because of these limitations and the complexity of automatic update mechanisms to see this service information, a large number of non-related service information for a requested query, and getting the required web information of services is a cumbersome problem. This paper proposes Semantic based Terms Relation Approach (STRA) for classifying information for effective classification of WIS on the web. The approach utilize Concept Terms Similarity (CTS) method for the most relevant terms in a service domain and construct a Related Terms Hierarchal Model (RTHM), which will be used for classification. A modified Naive Bayes classifier is used to perform the classification of the web information of services using RTHM, to categorize and present accurately. The experiment evaluation of the proposed approach shows an improvement in the classification of information and achieve a highly related matching results against different number of users queries.

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Section: Semantic Classification Using Rthmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Semantic based Terms Relation Research for Classification in Web Information Mining

Thota¹,

Mahalakshmi²

2019

IJRTE

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“…They converted the task of linking tweets with concepts in Wikipedia into a ranking problem of concepts that are related to the post [4]. Wikipedia was used to solve entity disambiguation for noisy short texts by Shirakawa as well [12].…”

Section: Introductionmentioning

confidence: 99%

Chinese Microblog Topic Detection through POS-Based Semantic Expansion

2018

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Abstract:A microblog is a new type of social media for information publishing, acquiring, and spreading. Finding the significant topics of a microblog is necessary for popularity tracing and public opinion following. This paper puts forward a method to detect topics from Chinese microblogs. Since traditional methods showed low performance on a short text from a microblog, we put forward a topic detection method based on the semantic description of the microblog post. The semantic expansion of the post supplies more information and clues for topic detection. First, semantic features are extracted from a microblog post. Second, the semantic features are expanded according to a thesaurus. Here TongYiCi CiLin is used as the lexical resource to find words with the same meaning. To overcome the polysemy problem, several semantic expansion strategies based on part-of-speech are introduced and compared. Third, an approach to detect topics based on semantic descriptions and an improved incremental clustering algorithm is introduced. A dataset from Sina Weibo is employed to evaluate our method. Experimental results show that our method can bring about better results both for post clustering and topic detection in Chinese microblogs. We also found that the semantic expansion of nouns is far more efficient than for other parts of speech. The potential mechanism of the phenomenon is also analyzed and discussed.

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“…The ambiguities related to search query can be removed by prompting user to select from the different contexts of the queries. For example an apple may resemble to a fruit family or a Company or some other ambiguity [3].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach

Rajeshwarkar¹,

Nagori²

2016

IJCA

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The Triangular Search approach aims at recalculating authenticity of the Search Results provided by the Google API with the help of Semantic similarity provided by Wikipedia API and calculating the cosine similarity between the Document Vectors and query string vectors using enhanced approach of Tf-Idf that reduces calculation involved in it. The Search Engine Optimization traces anchor texts that are the values between tag of HTML and body texts of a web page. Using the Vector Space Model, the Term frequency and Inverse document frequency are calculated along with the Page ranking algorithm to get the Search Results. But consideration of anchor texts in search engine optimization techniques leads to some of the non-relevant body texts of a document. Also the top results of a search engine include trending and e-commerce links other than sponsored links but the intent of search is not considered. This approach proposes and gains user intents behind the search thereby focusing on providing intent related search results.

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Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes

Cited by 44 publications

References 26 publications

A Semantic based Terms Relation Research for Classification in Web Information Mining

A Semantic based Terms Relation Research for Classification in Web Information Mining

Chinese Microblog Topic Detection through POS-Based Semantic Expansion

Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach

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