This paper presents a method for computing sentence similarity based on semantic nets and corpus statistics. The method considers both semantic and word order information in sentences. It uses a structured lexical database to model human common sense knowledge and corpus statistics to adapt to different domains. The proposed method calculates semantic similarity using information from a lexical database and corpus statistics. It also incorporates word order information to capture syntactic aspects of sentences. The method is tested on two sets of sentence pairs, demonstrating significant correlation with human intuition. The method is efficient, dynamic, and adaptable across various application domains. It outperforms traditional methods that rely on high-dimensional spaces and require manual preprocessing. The method is suitable for applications involving text knowledge representation and discovery. The results show that the proposed method provides a similarity measure that aligns with human perceptions of sentence similarity. The method is based on semantic similarity between words and word order similarity between sentences. It uses a combination of semantic and syntactic information to compute sentence similarity. The method is implemented using semantic nets and corpus statistics, with WordNet and the Brown Corpus as data sources. The method is evaluated using human similarity ratings for a dataset of 30 sentence pairs, showing promising results. The method is effective in capturing sentence meaning regardless of word co-occurrence. It is suitable for applications such as text summarization, text categorization, and machine translation. The method is adaptable to different domains and can be used in various text-related applications. The method is efficient, dynamic, and provides a similarity measure that aligns with human intuition. The method is based on semantic and syntactic information, and it uses a combination of semantic similarity and word order similarity to compute sentence similarity. The method is implemented using semantic nets and corpus statistics, with WordNet and the Brown Corpus as data sources. The method is evaluated using human similarity ratings for a dataset of 30 sentence pairs, showing promising results. The method is effective in capturing sentence meaning regardless of word co-occurrence. It is suitable for applications such as text summarization, text categorization, and machine translation. The method is adaptable to different domains and can be used in various text-related applications.This paper presents a method for computing sentence similarity based on semantic nets and corpus statistics. The method considers both semantic and word order information in sentences. It uses a structured lexical database to model human common sense knowledge and corpus statistics to adapt to different domains. The proposed method calculates semantic similarity using information from a lexical database and corpus statistics. It also incorporates word order information to capture syntactic aspects of sentences. The method is tested on two sets of sentence pairs, demonstrating significant correlation with human intuition. The method is efficient, dynamic, and adaptable across various application domains. It outperforms traditional methods that rely on high-dimensional spaces and require manual preprocessing. The method is suitable for applications involving text knowledge representation and discovery. The results show that the proposed method provides a similarity measure that aligns with human perceptions of sentence similarity. The method is based on semantic similarity between words and word order similarity between sentences. It uses a combination of semantic and syntactic information to compute sentence similarity. The method is implemented using semantic nets and corpus statistics, with WordNet and the Brown Corpus as data sources. The method is evaluated using human similarity ratings for a dataset of 30 sentence pairs, showing promising results. The method is effective in capturing sentence meaning regardless of word co-occurrence. It is suitable for applications such as text summarization, text categorization, and machine translation. The method is adaptable to different domains and can be used in various text-related applications. The method is efficient, dynamic, and provides a similarity measure that aligns with human intuition. The method is based on semantic and syntactic information, and it uses a combination of semantic similarity and word order similarity to compute sentence similarity. The method is implemented using semantic nets and corpus statistics, with WordNet and the Brown Corpus as data sources. The method is evaluated using human similarity ratings for a dataset of 30 sentence pairs, showing promising results. The method is effective in capturing sentence meaning regardless of word co-occurrence. It is suitable for applications such as text summarization, text categorization, and machine translation. The method is adaptable to different domains and can be used in various text-related applications.