Embodiments of the present disclosure provide a method and an apparatus for recognizing a medical entity in a medical text. In this method, the medical text is segmented into a plurality of words. Each of the words is used as a target word. A local and a global annotation feature of the target word are determined. The local annotation feature includes the target word. The global annotation feature includes a relationship between the target word and the recognized medical entity. Next, a label for the target word is determined from candidate labels based on the local and global annotation feature. Next, a combination relationship between the target word and a previous word of the target word is obtained based on the label for the target word. The combination relationship includes combination and non-combination. Next, the combined words are recognized as the medical entity based on the combination relationship.

Embodiments of the present disclosure provide a method and an apparatus for recognizing a medical entity in a medical text. In this method, the medical text is segmented into a plurality of words. Each of the words is used as a target word. A local and a global annotation feature of the target word are determined. The local annotation feature includes the target word. The global annotation feature includes a relationship between the target word and the recognized medical entity. Next, a label for the target word is determined from candidate labels based on the local and global annotation feature. Next, a combination relationship between the target word and a previous word of the target word is obtained based on the label for the target word. The combination relationship includes combination and non-combination. Next, the combined words are recognized as the medical entity based on the combination relationship.

Systems and techniques that facilitate parameterized neighborhood memory adaptation for semantic role labeling are provided. In various embodiments, a system can comprise a receiver component that can access a semantic role labeling model trained on a training dataset. In various aspects, the system can further comprise an execution component that can retrain a labeler of the semantic role labeling model based on a set of neighborhood parameters learned from the training dataset. In various instances, the execution component can execute, after retraining, the semantic role labeling model on an inputted sentence.

Provided is technology for assessing whether uttered speech detected from input speech is speech suited to a prescribed purpose. A method comprises detecting, from input speech including speech uttered by a speaker and noise, the uttered speech corresponding to the speech uttered by the speaker, extracting an acoustic feature of the uttered speech, generating, from the uttered speech, a speech recognition result set with a recognition score, generating, from the speech recognition result set with the recognition score, a speech recognition result word vector expression set and a speech recognition result part-of-speech vector expression set, generating a target utterance estimation model, providing, using the target utterance estimation model, a probability of the uttered speech being suited to the prescribed purpose, and outputting the uttered speech and the speech recognition result set with the recognition score, the the uttered speech suitable to the prescribed purpose.

A method of augmenting an information retrieval method including receiving a query, parsing the query to generate an instance graph, providing a plurality of constraint graphs, organized into at least two ranks, each constraint graph associated with a focus, determining at least one of the constraint graphs matching a structure of the instance graph, and annotating the query with the focus of each matching constraint graph of a highest rank among all matching constraint graphs.

A two-step algorithm for conducting near real-time fuzzy searches of a target on one or more large data sets is described. This algorithm includes the simplification of the data by removing grammatical constructs to bring the target search term (and the stored database) to their base elements and then performing a Levenstein comparison to create a subset of the data set that may be a match. Then performing a scoring algorithm while comparing the target to the subset of the data set to identify any matches.

An information generation method includes: receiving dictionary data used for morpheme analysis; and generating index information indicating relative positions of characters of each character included in a word registered in the dictionary data, a character at a head of the word, and a character at an end of the word based on the received dictionary data, by a processor.

An information generation method includes: receiving dictionary data used for morpheme analysis; and generating index information indicating relative positions of characters of each character included in a word registered in the dictionary data, a character at a head of the word, and a character at an end of the word based on the received dictionary data, by a processor.

A method and apparatus for generating information are disclosed. An implementation of the method includes: receiving a target text, the target text including an objective and descriptive information of the objective; performing a dependency syntax parsing on the target text to generate a dependency tree of the target text; matching predetermined syntactic structure tree with the dependency tree to obtain at least one triple, a triple including a subject, a predicate, and an object; and determining, based on words contained in a triple among the at least one triple and a predetermined weight of the syntactic structure tree matched to obtain the triple, a target triple among the at least one triple.

A device and method for providing recommended words for a character input by a user are provided. The method by which the device provides recommended words includes: receiving an input for inputting a character in a character input window; recommending at least one pseudo-morpheme including the input character by analyzing the input character; recommending at least one extended word including a selected pseudo-morpheme in response to receiving an input for selecting one of the at least one pseudo-morpheme; and displaying a selected extended word in response to receiving an input for selecting one of the at least one extended word.