A) Translate text from one language to another automatically. B) Generate human-like text responses. C) Analyze the sentiment of text. D) Convert speech to text.
A) Determine the sentiment or opinion expressed in text. B) Generating random text based on a given model. C) Analyzing the grammar and syntax of a sentence. D) Translating text from one language to another.
A) Syntax model B) Semantic model C) n-gram model D) Markov model
A) Recognizing different languages in a multilingual text. B) Determining the overall sentiment of a text. C) Identifying named entities in text such as names, organizations, and locations. D) Converting speech to text.
A) Generating new words based on existing ones. B) Reducing words to their base or root form. C) Identifying the relationship between words in a sentence. D) Analyzing the emotional tone of a text.
A) Lack of suitable hardware for processing language data. B) Inability to detect sentiment in text. C) Ambiguity in language that requires contextual understanding. D) Difficulty in translating between different languages.
A) Segmenting text into individual units such as words or phrases. B) Translating text from one language to another. C) Analyzing the grammatical structure of a sentence. D) Identifying the topic of a given text.
A) Recognizing named entities in text. B) Converting speech to text. C) Analyzing grammatical structure to determine the relationships between words. D) Generating synonyms for words.
A) A collection of text used for linguistic analysis. B) A specific type of dependency relationship between words. C) A type of syntax tree used in parsing algorithms. D) A method for translating between languages.
A) Identifying named entities in a text. B) Analyzing the syntax of a sentence. C) Creating a concise summary of a longer text document. D) Translating text between languages.
A) Point-of-sale tagging. B) Powerful optimization system tagging. C) Part-of-speech tagging. D) Public opinion survey tagging.
A) Determine the grammar of a sentence. B) Identify the sentiment of a given text. C) Generate new words based on existing vocabulary. D) Reduce words to their base or root form to improve analysis.
A) Noun B) Syntax C) Compiler D) Algorithm
A) Conducting sentiment analysis. B) Analyzing the syntax of a sentence. C) Translating text between languages. D) Identifying the relationships between words in a sentence and their semantic roles.
A) Transcription. B) Transformation. C) Transference. D) Tokenization.
A) Convolutional neural network (CNN). B) Recurrent neural network (RNN). C) Deep belief network (DBN). D) Radial basis function network (RBFN).
A) Identify named entities. B) Translate words between languages. C) Analyze sentence structure. D) Represent words as vectors to capture semantic meaning.
A) Analyze the sentiment of a given text. B) Translate text between languages. C) Parse the grammatical structure of a sentence. D) Identify specific entities such as names, organizations, and locations in text.
A) Latent Dirichlet Allocation. B) Language Development Assessment. C) Linear Discriminant Analysis. D) Localized Data Aggregation.
A) Image classification. B) Speech recognition. C) Random text generation. D) Information extraction.
A) Morphological analysis method. B) Neural machine translation. C) Rule-based translation algorithm. D) Symbol-based translation approach.
A) Statistical machine translation. B) Sentiment-based machine translation. C) Image-based machine translation. D) Rule-based machine translation.
A) Named entity recognition. B) Sentence segmentation. C) Dependency parsing. D) Topic modeling.
A) Java. B) Ruby. C) Python. D) C++. |