Operability of head-mounted display systems is enhanced by incorporating the following: a microphone which receives an utterance input by a person and outputs voice information; a character string generation unit which generates an uttered character string by converting the voice information into a character string; a specific utterance information storage unit which stores specific utterance information that associates at least one program to be started or stopped and/or at least one operating mode to be started or stopped, with specific utterances for starting or stopping each of the programs and/or operating modes; a specific utterance extraction unit which extracts a specific utterance included in the uttered character string with reference to the specific utterance information, and generates an extracted specific utterance signal indicating the extraction result; and a control unit which starts or stops a program or an operating mode with reference to the extracted specific utterance signal.

annotation data generation assisting system includes: an input/output device receiving an input through an interactive process; morphological analysis system and dependency parsing system performing morphological and dependency parsing on text data in text archive; first to fourth candidate generating units detecting a zero anaphor or a referring expression in the dependency relation of a predicate in a sequence of morphemes, identifying a position as an object of annotation and estimating candidates of expressions to be inserted by using language knowledge; a candidate DB storing estimated candidates; and an interactive annotation device reading candidates of annotation from candidate DB and annotate a candidate selected by an interactive process by input/output device.

The present disclosure provides an information extraction method and apparatus, an electronic device and a readable storage medium, and relates to the field of natural language processing technologies. The information extraction method includes: acquiring a to-be-extracted text; acquiring a sample set, the sample set including a plurality of sample texts and labels of sample characters in the plurality of sample texts; determining a prediction label of each character in the to-be-extracted text according to a semantic feature vector of each character in the to-be-extracted text and a semantic feature vector of each sample character in the sample set; and extracting, according to the prediction label of each character, a character meeting a preset requirement from the to-be-extracted text as an extraction result of the to-be-extracted text. The present disclosure can simplify steps of information extraction, reduce costs of information extraction and improve flexibility and accuracy of information extraction.

Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.

Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.

A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample. The method also includes training, using the normalized training data samples, a multilingual end-to-end speech recognition model to predict speech recognition results in the target script for corresponding speech utterances spoken in any of the different native languages associated with the plurality of training data sets.

Provided is a device including a memory storing information about sequences of a plurality of registered words; an input unit comprising input circuitry configured to receive an input of a text comprising a first eojeol not belonging to the plurality of registered words, wherein, in the first eojeol, a first word is attached to a first registered word that belongs to the plurality of registered words; and a controller configured to detect the first registered word from the first eojeol, to determine a predicted eojeol to be input after the text, based on the information about the sequences of the plurality of registered words and the detected first registered word and to control a display to display the predicted eojeol.

Provided is a device including a memory storing information about sequences of a plurality of registered words; an input unit comprising input circuitry configured to receive an input of a text comprising a first eojeol not belonging to the plurality of registered words, wherein, in the first eojeol, a first word is attached to a first registered word that belongs to the plurality of registered words; and a controller configured to detect the first registered word from the first eojeol, to determine a predicted eojeol to be input after the text, based on the information about the sequences of the plurality of registered words and the detected first registered word and to control a display to display the predicted eojeol.

Arabic Latinized is the first and only technique to learn Arabic Language that is based on conditioning the mind to convert Arabic to Latin, in reading and writing.

The Arabic alphabet: Consists of 28 letters and 1 auxiliary called Hamza (table in FIGS. 1 and 2).

“Arabic letters in common”; Nineteen, are pronounced as in Latin.

“Arabic letters in proper”: Nine, plus the auxiliary Hamza (10 letters), also called “Gluttural Letters”, they exist only in Arabic.

The correct way of pronouncing the Arabic “guttural Letters” with the anatomical site to produce those letters is illustrated in FIG. 3

“Hamza”, the 29.sup.th auxiliary Letter is pronounced a “guttural catch or pause” in the voice, as the letter “A” in “Apple” sounds; FIG. 3. It can be independent stand-alone “”, or add-on to any of the Vowel letters: Alef (#1), either above, written as “” or beneath, written as “” Waw (#27), only above, written as “” Ya' (#28), only above, written as “”

The technique of Arabic letters' conversion to Latin is illustrated in FIGS. 4 and 5.

The invention attended to every detail unique to the Arabic language, especially the symbols of Short Vowels (FIG. 6)

Arabic Latinized is the first and only technique to learn Arabic Language that is based on conditioning the mind to convert Arabic to Latin, in reading and writing.

The Arabic alphabet: Consists of 28 letters and 1 auxiliary called Hamza (table in FIGS. 1 and 2).

“Arabic letters in common”; Nineteen, are pronounced as in Latin.

“Arabic letters in proper”: Nine, plus the auxiliary Hamza (10 letters), also called “Gluttural Letters”, they exist only in Arabic.

The correct way of pronouncing the Arabic “guttural Letters” with the anatomical site to produce those letters is illustrated in FIG. 3

“Hamza”, the 29.sup.th auxiliary Letter is pronounced a “guttural catch or pause” in the voice, as the letter “A” in “Apple” sounds; FIG. 3. It can be independent stand-alone “”, or add-on to any of the Vowel letters: Alef (#1), either above, written as “” or beneath, written as “” Waw (#27), only above, written as “” Ya' (#28), only above, written as “”

The technique of Arabic letters' conversion to Latin is illustrated in FIGS. 4 and 5.

The invention attended to every detail unique to the Arabic language, especially the symbols of Short Vowels (FIG. 6)