A) Analyze the sentiment of text.
B) Generate human-like text responses.
C) Translate text from one language to another automatically.
D) Convert speech to text.

A) Determine the sentiment or opinion expressed in text.
B) Translating text from one language to another.
C) Analyzing the grammar and syntax of a sentence.
D) Generating random text based on a given model.

A) Markov model
B) Syntax model
C) Semantic model
D) n-gram model

A) Identifying named entities in text such as names, organizations, and locations.
B) Converting speech to text.
C) Recognizing different languages in a multilingual text.
D) Determining the overall sentiment of a text.

A) Identifying the relationship between words in a sentence.
B) Generating new words based on existing ones.
C) Reducing words to their base or root form.
D) Analyzing the emotional tone of a text.

A) Ambiguity in language that requires contextual understanding.
B) Lack of suitable hardware for processing language data.
C) Inability to detect sentiment in text.
D) Difficulty in translating between different languages.

A) Segmenting text into individual units such as words or phrases.
B) Analyzing the grammatical structure of a sentence.
C) Identifying the topic of a given text.
D) Translating text from one language to another.

A) Converting speech to text.
B) Analyzing grammatical structure to determine the relationships between words.
C) Recognizing named entities in text.
D) Generating synonyms for words.

A) A specific type of dependency relationship between words.
B) A type of syntax tree used in parsing algorithms.
C) A collection of text used for linguistic analysis.
D) A method for translating between languages.

A) Translating text between languages.
B) Identifying named entities in a text.
C) Creating a concise summary of a longer text document.
D) Analyzing the syntax of a sentence.

A) Public opinion survey tagging.
B) Part-of-speech tagging.
C) Point-of-sale tagging.
D) Powerful optimization system tagging.

A) Identify the sentiment of a given text.
B) Reduce words to their base or root form to improve analysis.
C) Generate new words based on existing vocabulary.
D) Determine the grammar of a sentence.

A) Syntax
B) Algorithm
C) Noun
D) Compiler

A) Conducting sentiment analysis.
B) Identifying the relationships between words in a sentence and their semantic roles.
C) Analyzing the syntax of a sentence.
D) Translating text between languages.

A) Tokenization.
B) Transformation.
C) Transference.
D) Transcription.

A) Radial basis function network (RBFN).
B) Recurrent neural network (RNN).
C) Deep belief network (DBN).
D) Convolutional neural network (CNN).

A) Translate words between languages.
B) Analyze sentence structure.
C) Represent words as vectors to capture semantic meaning.
D) Identify named entities.

A) Translate text between languages.
B) Identify specific entities such as names, organizations, and locations in text.
C) Parse the grammatical structure of a sentence.
D) Analyze the sentiment of a given text.

A) Linear Discriminant Analysis.
B) Latent Dirichlet Allocation.
C) Language Development Assessment.
D) Localized Data Aggregation.

A) Information extraction.
B) Speech recognition.
C) Random text generation.
D) Image classification.

A) Morphological analysis method.
B) Symbol-based translation approach.
C) Neural machine translation.
D) Rule-based translation algorithm.

A) Sentiment-based machine translation.
B) Rule-based machine translation.
C) Statistical machine translation.
D) Image-based machine translation.

A) Named entity recognition.
B) Dependency parsing.
C) Topic modeling.
D) Sentence segmentation.

A) Ruby.
B) Java.
C) Python.
D) C++.