Siamese LSTM for evaluating semantic similarity between sentences of the Quora Question Pairs Dataset. Topicsnlp deep-learning keras lstm ResourcesReadme Activity Stars254 stars Watchers11 watching Forks70 forks Report repository Releases No releases published Packages No packages published Languages Python 100.0% Footer © 2025 GitHub, Inc. Foot...
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% ...
embedding_next= sentences[i + 1]['combined_sentence_embedding']#Calculate cosine similaritysimilarity =cosine_similarity(embedding_current, embedding_next)#Convert to cosine distancedistance = 1 -similarity distances.append(distance)#Store distance in the dictionarysentences[i]['distance_to_next'] =dis...
"Semantic Text Similarity" Task These datasets consider the semantic similarity of independent pairs of texts (typically short sentences) and share a precise similarity metric definition of assigning a number between 0 to 5 to each pair denoting the level of similarity/entailment. ...
The cosine similarity between sentences, X and Y can be calculated as Eq. 7.(7)CosineSimilarity=CS=∑i=1NXiYi∑i=1NXi2∑i=1NYi2where Xi and Yi are the constituents of vectors X and Y, respectively. Algorithm 1 Headline and news summary measure Require: News articles Ensure Real news...
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...
activities of neurons for word pairs based on their vectoral cosine distance within the 300-dimensional embedding space (z-scored activity change over percentile cosine similarity, red regression line; Pearson’s correlation,r = 0.17). Right: correlation between vectoral cosine distances in the...
Secondly, we model the local sequential order between consecutive sentences by using a 1D convolutional neural network. 3.2.1. SA-GCN: Graph convolutional network with self-attention We define the semantic relevance graph as G=V,E , where V is the set of graph nodes and E is the set of ...
introduced textrank [13], an algorithm that uses several statistical features and one specialized function that calculates a weighted sum for calculating the similarity between sentences [14]. Query based text summarization In this technique, the summaries are generated by a scoring mechanism. The ...
similarities.levenshtein –Fast soft-cosine semantic similarity search similarities.fastss –Fast Levenshtein edit distance test.utils –Internal testing functions topic_coherence.aggregation –Aggregation module topic_coherence.direct_confirmation_measure –Direct confirmation measure module topic_coherence.indirect...