Abstract: Systems and methods for dynamically generating a predicted similarity score for a pair of input sequences. A predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a target region match indication for the pair of input sequences, a fuzzy match score for the pair of input sequences, a character-level match score for the pair of input sequences, one or more similarity ratio occurrence indicators for the pair of input sequences, and a harmonic mean score of the fuzzy match score for the pair of input sequences and the token-level similarity probability score for the pair of input sequences.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for dynamically generating a predicted similarity score for a pair of input sequences. According to one aspect, a predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a target region match indication for the pair of input sequences, a fuzzy match score for the pair of input sequences, a character-level match score for the pair of input sequences, one or more similarity ratio occurrence indicators for the pair of input sequences, and a harmonic mean score of the fuzzy match score for the pair of input sequences and the token-level similarity probability score for the pair of input sequences.

Abstract: Described herein is a method and model system to improve the quality of search recall in the social media communication posts that often contains the local language words written in Romanized English script. A Deep Learning Transformer based model architecture and algorithm improves the search recall for a given English query. The model is trained to find named entities from post and queries, and these entities are compared to find the matching score using a specially designed model that takes into account the post's recency and its cleanliness score. The cleanliness score is obtained from a trained LSTM based model. The input English query is expanded to a set of equivalent queries by including contextually nearest words. The number of nearest words can be controlled using a slider mechanism.

Abstract: Described herein is a method of context aware chat categorization for business decisions. A business category of the chat presented by the viewer/user on a platform is predicted and groups of chats having similar context and created and arranged in an ordered score indicative of importance. The categorization method includes applying LSTM's in parallel with shared embeddings on said user data, applying an LSTM technique to determine sentence similarity, applying the user's social connectivity in the form of Eigen-centrality of its connectivity on said platform, determining the customised loss function, grouping of chats in categories based on context, context based grouping of chats wherein context is obtained from chat description, and determining attention score based on textual representation of human emotions such as emojis, repetitive characters, and words for each group of chats in a category.