Semantic similarityFollowing the Principle of Compositionality, the meaning of a complex expression is influenced, to some extent, not only by the meanings of its individual words, but also the structural way the words are assembled. Compositionality has been a central research issue for linguists ...
In this paper, a new method, called denoising distant supervision, is presented to reduce the wrong mappings between sentences and triples by taking the semantic similarity between sentences and the label of triples' predicate into account. For this purpose, different semantic similarity measures are...
The above method allows us to find the most appropriate sense for each word in a sentence. To compute the similarity between two sentences, we base the semantic similarity between word senses. We capture semantic similarity between two word senses based on the path length similarity. In WordNet...
Siamese LSTM for evaluating semantic similarity between sentences of the Quora Question Pairs Dataset. - likejazz/Siamese-LSTM
Most of the semantic similarity between the sentences of the five translators is more than 80%, this demonstrates that the main body of the five translations captures the semantics of the original Analects quite well. Conversely, the outcomes of semantic similarity calculations falling below 80% ...
It is debated that, when the word-level edit distance is very small, it is wiser to measure dissimilarity than similarity. Using knowledge bases along with common natural language processing tools, the proposed method tries to enhance the accuracy of measuring similarity between two sentences. We ...
hybrid model to improve Information Content (IC) related metrics of semantic similarity between words, named IC+SP, based on the essential hypothesis that IC and the shortest path are two relatively independent semantic evidences and have approximately equal influences to the semantic similarity metric...
Measuring the semantic similarity between various text components like words, sentences, or documents plays a significant role in a wide range of NLP tasks like informationretrieval, text summarization, text classification, essay evalution, machine translation, question answering, among others. ...
Recently, there have been emerging tasks that take advantage of short-text semantic similarity (STSS) and require to assess the degree of similarity between sentences or text snippets. Some example tasks getting benefit from STSS are question answering [6], short answer scoring [4], machine tra...
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are...