Learning to Compute Semantic Relatedness Using Knowledge from Wikipedia

We present in this paper an approach to assessing student paraphrases in the intelligent tutoring system iSTART. The approach is based on measuring the semantic similarity between a student paraphrase and a reference text, called the textbase. The semantic similarity is estimated using knowledge-based word relatedness measures. The relatedness measures rely on knowledge encoded in Word-Net, a lexical database of English. We also experiment with weighting words based on their importance. The word importance information was derived from an analysis of word distributions in 2,225,726 documents from Wikipedia. Performance is reported for 12 different models which resulted from combining 3 different relatedness measures, 2 word sense disambiguation methods, and 2 word-weighting schemes. Furthermore, comparisons are made to other approaches such as Latent Semantic Analysis and the Entailer.

Semantic Similarity is one of the highest levels of NLP. STS has significant advantages in NLP applications like information retrieval, information extraction, text summarization, data mining, machine translation, and other tasks. This research aims to present a deep learning approach for capturing semantic textual similarity (STS) in the Guragigna. The methodology involves collecting a Guragigna language corpus and preprocessing the text data and text representation is done using the Universal Sentence Encoder (USE), along with word embedding techniques including Word2Vec and GloVe and mean Square Error (MSE) is used to measure the performance. In the experimentation phase, models like LSTM, Bidirectional RNN, GRU, and Stacked RNN are trained and evaluated using different embedding techniques. The results demonstrate the efficacy of the developed models in capturing semantic textual similarity in the Guragigna language. Across different embedding techniques, including Word2Vec, GloVe, and USE, the Bidirectional RNN model with USE embedding achieves the lowest MSE of 0.0950 and the highest accuracy of 0.9244. GloVe and Word2Vec embedding also show competitive performance with slightly higher MSE and lower accuracy. The Universal Sentence Encoder consistently emerges as the top-performing embedding across all RNN architectures. The research results demonstrate the effectiveness of LSTM, GRU, Bi RNN, and Stacked RNN models in measuring semantic textual similarity in the Guragigna language.

LWCR: multi-Layered Wikipedia representation for Computing word Relatedness

Query expansion is a classical topic in the field of information retrieval, which is proposed to bridge the gap between searchers' information intents and their queries. Previous researches usually expand queries based on document collections, or some external resources such as WordNet and Wikipedia [1, 2, 3, 4, 5]. However, it seems that independently using one of these resources has some defects, document collections lack semantic information of words, while WordNet and Wikipedia may not include domain-specific knowledge in certain document collection. Our work aims to combine these two kinds of resources to establish an expansion model which represents not only domain-specific information but also semantic information. In our preliminary experiments, we construct a two-layer word graph and use Random-Walk algorithm to calculate the weights of each term in pseudo-relevance feedback documents, then select the highest weighted term to expand original query. The first layer of the word graph contains terms in related documents, while the second layer contains semantic senses corresponding to these terms. These terms and semantic senses are treated as vertices of the graph and connected with each other by all possible relationships, such as mutual information and semantic similarities. We utilized mutual information, semantic similarity and uniform distribution as the weight of term-term relation, sense-sense relation and word-sense relation respectively. Though these experiments show that our expansion outperform original queries, we are troubled with some difficult problems. Given the framework of semantic graph model, we need more effort to find out an optimal graph to represent the relationships between terms and their semantic senses. We utilized a two-layer graph model in our preliminary research, where terms from different documents are treated equally. Maybe we can introduce the document as a third layer in future work, where we can differ the same terms in different documents according to document relevance and context. Then we need appropriately represent initial weights of this words, senses and relationships. Various measures for weights of terms and term relations have been proved effective in other information retrieval tasks, such as TFIDF, mutual information (MI), but there is little research on weights for semantic senses and their relations. For polysemous words, we add all of their semantic senses to the graph and assume that these senses are uniformly distributed. Actually, it is not precise for a word in a special document and query. As we know, a polysemous word may have only one or two senses in a document, and they are not uniformly distributed. Give a word, what we should do is to determine its word senses in a relevant document and estimate the distribution of these senses. Word sense disambiguation may help us in this problem. Then, there are many methods to compute word similarity according to WordNet, which we use to represent the weights of relationships between word senses. Varelas et al implemented some popular methods to compute semantic similarity by mapping terms to an ontology and examining their relationships in that ontology [4]. We also need to know which algorithm for semantic similarity is most suitable for our model. Additional, WordNet is suitable to calculate word similarity but not suitable to measure word relevance. The inner hyperlinks of Wikipedia could help us to calculate word relevance. We wish to find an effective way to combine the similarity measure from WordNet and relevance measure from Wikipedia, which may completely reflect word relationships.

Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidi-mensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.

Instance-Based Zero-Shot learning for semi-Automatic MeSH indexing

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

Existing work in the semantic relatedness literature has already considered various information sources such as WordNet, Wikipedia and Web search engines to identify the semantic relatedness between two words. We will show that existing semantic relatedness measures might not be directly applicable to microblogging content such as tweets due to i) the informality and short length of microblogging content, which can lead to shift in the meaning of words when used in microblog posts, ii) the presence of non-dictionary words that have their semantics defined/evolved by the Twitter community. Therefore, we propose the Twitter Space Semantic Relatedness (TSSR) technique that relies on the latent relation hypothesis to measure semantic relatedness of words on Twitter. We construct a graph representation of terms in tweets and apply a random walk procedure to produce a stationary distribution for each word, which is the basis for relatedness calculation. Our experiments examine TSSR from three different perspectives and show that TSSR is better suited for Twitter analytics compared to the standard semantic relatedness techniques.

Semantic similarity is an essential concept that widen across various fields such as artificial intelligence, natural language processing, information retrieval, relation extraction, document clustering and automatic data extraction. The study proposed in this paper focus on semantic similarity and counter measure on question answering task. Despite the usefulness of semantic similarity in these applications, finding the exact meaning between the words from question and answer pair has become a major challenge in question answering system. To recognize various relationships those exist between question and answer pair, measuring semantically is essential. We present study using a match maker algorithm for similarity measurement between question-answer pair. The study compares similarity measurement using match maker algorithm as against semantic similarity measure on Miller-Charles bench marked data set. This paper present study about different types of algorithm used to measure the semantic similarity between the words and those result compare with match maker algorithm. The experiments on real datasets shows matchmaker algorithm works better than web-based semantic similarity measure.

Semantic relatedness is a well known problem with its sig- nicance ranging from computational linguistics to Natural language Processing applications. Relatedness computation is restricted by the amount of common sense and background knowledge required to relate any two terms. This paper proposes a novel model of relatedness using context prole built on features extracted from encyclopedic knowledge. Proposed research makes use of Wikipedia to represent the context of a word in the high dimensional space of Wikipedia labels. Semantic relat- edness of a word pair is then assessed by comparing their corresponding context proles based on three dierent weighting schemes using tradi- tional Cosine similarity metrics. To evaluate proposed relatedness ap- proach, three well known benchmark datasets are used and it is shown that Wikipedia article contents can be used eectively to compute term relatedness. The experiments demonstrate that the proposed approach is computationally cheap as well as eective when correlated with human judgments.

Reliability of Wikipedia as a medication information source for pharmacy students

In this paper the authors suggest an novel approach that uses Wikipedia to measure the semantic relatedness between Chinese named entities, such as names of persons, books, softwares, etc. The relatedness is measured through articles in Wikipedia that are related to the named entities. The authors select a set of ''definition words'' which are hyperlinks from these articles, and then compute the relatedness between two named entities as the relatedness between two sets of definition words. The authors propose two ways to measure the relatedness between two definition words: by Wiki-articles related to the words or by categories of the words. Proposed approaches are compared with several other baseline models through experiments. The experimental results show that this method renders satisfactory results.

Semantic relatedness means the degree of the nearness of two documents or two terms based on the sameness of their meaning or semantic contents. A method for computing semantic relatedness is crucial in the web mining applications such as search engine, recommendation systems and information retrieval systems, and also in the area of natural language processing. This paper proposes a new technique for measuring semantic relatedness between two terms. It uses Wikipedia as a source of structured world knowledge about the terms of interest while ontologies are used to retrieve the semantically related information in most systems. The proposed method used spreading activation approach over the taxonomy of Wikipedia categories to get the measures of semantic relatedness. Spreading activation strategy is a popular approach in associative retrieval. The proposed method is experienced with the benchmark dataset to show the comparison our approach with some existing approaches.

The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current research objective is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by the method are compared with those on the benchmark using the Spearman's rank coefficient. This methodology works the other way round and uses this coefficient to tune the proximity weights. The tuning process is controlled by a genetic algorithm using the Spearman's rank coefficient as the fitness function. The genetic algorithm has its own set of parameters which also need to be tuned. Bootstrapping is based on a statistical method for generating samples that is used in this methodology to enable a large number of repetitions of the genetic algorithm, exploring the results of alternative parameter settings. This approach raises several technical challenges due to its computational complexity. This paper provides details on the techniques used to speedup this process. The proposed approach was validated with the WordNet 2.0 and the WordSim-353 data set. Several ranges of parameters values were tested and the obtained results are better than the state of the art methods for computing semantic relatedness using the WordNet 2.0, with the advantage of not requiring any domain knowledge of the ontological graph.

The semantic similarity estimation is an important basic research topic in conceptual modeling. This paper proposes a high-efficiency and asymmetrical feature mapping model for computing semantic similarity. Firstly, to improve efficiency, we take the edge as the main information source of mapped features for concepts. Then, to avoid the computed deviation with the human judgment in the low score range is more significant than that in the high score range, we map the commonalities between concepts only into the single information source of the weighted depth of their Least Common Subsumer (LCS), and map their differences into three types of information sources, including their weighted path, density and encoding distance, to improve the ability to distinguish between different concepts. To further improve efficiency, we consider only the direct hyponyms of LCS as its density, rather than all of Its hyponyms. Moreover, the edge w eight is introduced into the calculation of depths and paths to reduce the influence of high-layer edges. The experimental results show that the proposed model is an excellent semantic similarity method with high computational efficiency and high measurement accuracy on both the common ontology WordNet and the biomedical ontology SNOMED CT.