35 results about "Bigram" patented technology

An apparatus generates a statistical class sequence model called A class bi-multigram model from input training strings of discrete-valued units, where bigram dependencies are assumed between adjacent variable length sequences of maximum length N units, and where class labels are assigned to the sequences. The number of times all sequences of units occur are counted, as well as the number of times all pairs of sequences of units co-occur in the input training strings. An initial bigram probability distribution of all the pairs of sequences is computed as the number of times the two sequences co-occur, divided by the number of times the first sequence occurs in the input training string. Then, the input sequences are classified into a pre-specified desired number of classes. Further, an estimate of the bigram probability distribution of the sequences is calculated by using an EM algorithm to maximize the likelihood of the input training string computed with the input probability distributions. The above processes are then iteratively performed to generate statistical class sequence model.

The invention discloses an automatic microblog text abstracting method based on unsupervised key binary word extraction. The automatic microblog text abstracting method comprises the steps of preprocessing a microblog; standardizing a binary word; extracting a key binary word based on a mixed TF-IDF (term frequency-inverse document frequency), TexRank and an LDA (local data area); sequencing sentences based on the intersection similarity and a mutual information strategy; extracting abstract sentences based on a similarity threshold value; generating abstract by reasonably combining the abstract sentences. According to the automatic microblog text abstracting method, the binary word is used as a minimum vocabulary unit, and the binary word has richer text information than words, so that the sentences based on the key binary word is higher in noise immunity and accuracy than the sentences based on key word extraction; meanwhile, when the abstract sentences are extracted, the similarity threshold value is introduced to control redundancy, so that the abstract is higher in recall rate. The abstract generated by the method is accurate, simple and comprehensive; the efficiency and the quality that a user acquires knowledge are obviously improved, and the time of the user is greatly saved.

A method for storing bigram word indexes of a language model for a consecutive speech recognition system (200) is described. The bigram word indexes (321) are stored as a common two-byte base with a specific one-byte offset to significantly reduce storage requirements of the language model data file. In one embodiment the storage space required for storing the bigram word indexes (321) sequentially is compared to the storage space required to store the bigram word indexes as a common base with specific offset. The bigram word indexes (321) are then stored so as to minimize the size of the language model data file.

A method for generating a bigram database may include receiving domain names, tokenizing the domain names, generating token bigrams from the tokenized domain names, filtering the token bigrams, ranking the token bigrams, and storing the filtered and ranked token bigrams in a bigram database. A method for suggesting alternative domain names may include receiving a requested domain name, tokenizing the requested domain name to divide the requested domain name into a series of tokens, retrieving token bigrams for tokens of the requested domain name, generating alternative domain name suggestions based on the token bigrams and the requested domain name, ranking the alternative domain name suggestions, and outputting at least one of the alternative domain name suggestions.

The present invention discloses a method for semantically processing speech for speech recognition purposes. The method can reduce an amount of memory required for a Viterbi search of an N-gram language model having a value of N greater than two and also having at least one embedded grammar that appears in a multiple contexts to a memory size of approximately a bigram model search space with respect to the embedded grammar. The method also reduces needed CPU requirements. Achieved reductions can be accomplished by representing the embedded grammar as a recursive transition network (RTN), where only one instance of the recursive transition network is used for the contexts. Other than the embedded grammars, a Hidden Markov Model (HMM) strategy can be used for the search space.

This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

Method for creating a language model capable of preventing deterioration of quality caused by the conventional back-off to unigram. Parts-of-speech with the same display and reading are obtained from a storage device (206). A cluster (204) is created by combining the obtained parts-of-speech. The created cluster (204) is stored in the storage device (206). In addition, when an instruction (214) for dividing the cluster is inputted, the cluster stored in the storage device (206) is divided (210) in accordance with to the inputted instruction (212). Two of the clusters stored in the storage device are combined (218), and a probability of occurrence of the combined clusters in the text corpus is calculated (222). The combined cluster is associated with the bigram indicating the calculated probability and stored into the storage device.

The invention provides a method and system for finding out new words. The method comprises the following steps of: based on a bigram language model, respectively extracting bigram elements of a foreground corpus; respectively obtaining statistical information of the foreground corpus; filtering the bigram elements according to the statistical information and a first pre-set rule; expanding the remained bigram elements in the foreground corpus by using an n-gram language model and a second pre-set rule, wherein re-counting a background corpus is unnecessary during the updating of n-gram elements; preventing from re-finding out existing new words in the background corpus; judging boundaries of the new words according to the second pre-set rule; and removing garbage bigram elements and n-gram elements. The method is used simply and easily. The manual correction burden is reduced.