Furthermore, the performance of this proposed similarity measurement method was accomplished by employing precision, recall, and F-measure. Technically, we developed a measure of similarity Jaccard with Prolog programming language to compare similarity between sets of data. Thus, this paper proposed the similarity measurement method between words by deploying Jaccard Coefficient. Consequently, a similarity measurement between keywords and index terms is essentially performed to facilitate searchers in accessing the required results promptly. Particularly, information retrieval results as documents are typically too extensive, which affect on accessibility of the required results for searchers. Additionally, an index of search engines has to be updated on most recent information as it is constantly changed over time. This allows users to specify the search criteria as well as specific keywords to obtain the required results. Presently, information retrieval can be accomplished simply and rapidly with the use of search engines. The datasets and codes used in this work are freely available at combination of bigram and trigram, and generates suggestions based on the Cosine similarity measure with the accuracy rate of 94.29% individually. The grammar checker detects errors based on language model probability i.e. The spell checker uses the Double Metaphone algorithm and Edit distance based on the distributed lexicons and numerical suffix dataset to detect all types of Bangla spelling mistakes with an accuracy rate of 97.21% individually. Based on these corpus and lexicon, we have developed a combined spell and grammar checker application that simultaneously detects distinct spelling and grammatical mistakes and provides appropriate suggestions for both as well. At first, a full-fledged and generalised Bangla monolingual corpus comprising over 100 million words has been built by scraping reputed, diversified online sources and then an extensive Bangla lexicon consisting of over 1 million unique words has been extracted from that corpus. Considering the lack of research efforts, we demonstrate the development of a comprehensive Bangla spell and grammar checker with necessary resources. Load a pre-trained scRNN spell corrector instance.A spell and grammar checker is profoundly essential for diverse publications especially for Bangla language in particular as it is spoken by millions of native speakers around the world. loadSCRNNSpellCorrector ( filepath, compact=True ) ¶ optimizer ( str) – optimizer (Default: “rmsprop”).dropout_rate ( float) – dropout rate (Default: 0.01).nb_epoch ( int) – number of epochs (Default: 100).Train ( text, nb_epoch=100, dropout_rate=0.01, optimizer='rmsprop' ) ¶ Generator that outputs the numpy vectors for training Generator that outputs the numpy vectors for correctionĪ generator that output numpy vectors for the text for training. ModelNotTrainedException is raised if the model has not been trained. Parameters:Ī generator that output numpy vectors for the text for correction. Recommend a spell correction to given the word. Keisuke Sakaguchi, Kevin Duh, Matt Post, Benjamin Van Durme, “Robsut Wrod Reocginiton via semi-Character Recurrent Neural Networ,” arXiv:1608.02214 (2016). ScRNN (semi-character-level recurrent neural network) Spell Corrector. SCRNNSpellCorrector ( operation, alph="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz., specialsignals=, concatcharvec_encoder=None, batchsize=1, nb_hiddenunits=650 ) ¶ loadSCRNNSpellCorrector ( '/path/to/spellscrnn.bin' ) class. Given the text, train the spell corrector. List of words that can be found in the training corpus Recommend a spelling correction to the given word Parameters:įilter away the words that are not found in the training corpus. List potential candidates for corrected spelling to the given words. Probability of the word sampled randomly in the corpus ( ) P ( word ) ¶Ĭompute the probability of the words randomly sampled from the training corpus. Spell corrector described by Peter Norvig in his blog. correct ( 'oranhe' ) # gives "orange" class. NorvigSpellCorrector () > norvig_corrector.
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