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Questions and Answers
Sentiment analysis, also known as ________, uses natural language processing to identify and classify emotions in text.
Sentiment analysis, also known as ________, uses natural language processing to identify and classify emotions in text.
opinion mining
The three levels of sentiment analysis are ________, ________, and ________.
The three levels of sentiment analysis are ________, ________, and ________.
document level, sentence level, and entity and aspect level
Sentiment analysis aims to classify text into three categories: ________, ________, and ________.
Sentiment analysis aims to classify text into three categories: ________, ________, and ________.
positive, negative, neutral
The ________ level of sentiment analysis evaluates the overall sentiment of a document.
The ________ level of sentiment analysis evaluates the overall sentiment of a document.
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One major challenge in sentiment analysis is understanding ________, such as 'not bad,' which can invert the sentiment.
One major challenge in sentiment analysis is understanding ________, such as 'not bad,' which can invert the sentiment.
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The process of assigning a lexical class marker to each word in a corpus is called _______.
The process of assigning a lexical class marker to each word in a corpus is called _______.
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Words like 'in' and 'on' are part of the _______ class, which has a fixed membership.
Words like 'in' and 'on' are part of the _______ class, which has a fixed membership.
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The _______ tagset consists of 45 tags and is widely used in NLP.
The _______ tagset consists of 45 tags and is widely used in NLP.
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Rule-based POS tagging relies on _______ crafted based on linguistic knowledge.
Rule-based POS tagging relies on _______ crafted based on linguistic knowledge.
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In probabilistic sequence models, _______ assumes the next state depends only on the current state.
In probabilistic sequence models, _______ assumes the next state depends only on the current state.
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Training data is typically split into _______ for model training and _______ for testing.
Training data is typically split into _______ for model training and _______ for testing.
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The metric that calculates the harmonic mean of precision and recall is called _______.
The metric that calculates the harmonic mean of precision and recall is called _______.
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_______ is a problem where the contexts to be tagged do not appear in the training data.
_______ is a problem where the contexts to be tagged do not appear in the training data.
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A Markov Chain cannot represent _______ as it uniquely determines the path through states.
A Markov Chain cannot represent _______ as it uniquely determines the path through states.
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The extension of Markov Chains that includes hidden states is called _______.
The extension of Markov Chains that includes hidden states is called _______.
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In HMM, the probability of observing a specific output given a state is known as _______.
In HMM, the probability of observing a specific output given a state is known as _______.
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The _______ algorithm is used to compute the probability of an observation sequence.
The _______ algorithm is used to compute the probability of an observation sequence.
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A left-to-right HMM commonly used in speech recognition is called a _______ HMM.
A left-to-right HMM commonly used in speech recognition is called a _______ HMM.
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Probabilistic Context-Free Grammar (PCFG) is a CFG variant where each production rule has an associated _______.
Probabilistic Context-Free Grammar (PCFG) is a CFG variant where each production rule has an associated _______.
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Treebanks are corpora annotated with _______ trees.
Treebanks are corpora annotated with _______ trees.
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The _______ pointers in the Viterbi algorithm trace the best path through the states.
The _______ pointers in the Viterbi algorithm trace the best path through the states.
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Statistical parsing uses a _______ model to assign probabilities to parse trees.
Statistical parsing uses a _______ model to assign probabilities to parse trees.
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A probabilistic version of CFG is called _______.
A probabilistic version of CFG is called _______.
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The _______ grammar is a corpus annotated with parse trees, commonly used for supervised learning.
The _______ grammar is a corpus annotated with parse trees, commonly used for supervised learning.
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In statistical parsing, the probability of a sentence is the _______ of the probabilities of all its derivations.
In statistical parsing, the probability of a sentence is the _______ of the probabilities of all its derivations.
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The _______ algorithm helps efficiently determine the most probable derivation for a sentence in PCFG.
The _______ algorithm helps efficiently determine the most probable derivation for a sentence in PCFG.
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_______ parsing starts from the root of the parse tree and applies grammar rules to generate possible trees.
_______ parsing starts from the root of the parse tree and applies grammar rules to generate possible trees.
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_______ parsing starts from terminal symbols and works backward to find the root.
_______ parsing starts from terminal symbols and works backward to find the root.
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The F1 score is the harmonic mean of _______ and _______.
The F1 score is the harmonic mean of _______ and _______.
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Sentiment analysis is often applied to sources like social media posts, ________, and ________.
Sentiment analysis is often applied to sources like social media posts, ________, and ________.
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________ is an information-theoretic measure used to identify word associations or collocations in text.
________ is an information-theoretic measure used to identify word associations or collocations in text.
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________ is a Python library commonly used for implementing sentiment analysis using tools like classifiers and feature extraction.
________ is a Python library commonly used for implementing sentiment analysis using tools like classifiers and feature extraction.
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Positive sentiment words include ________, ________, and ________.
Positive sentiment words include ________, ________, and ________.
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Named Entity Recognition seeks to classify entities in text into predefined categories like ________, ________, and ________.
Named Entity Recognition seeks to classify entities in text into predefined categories like ________, ________, and ________.
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The ________ approach to NER uses predefined vocabulary lists to match entities in text.
The ________ approach to NER uses predefined vocabulary lists to match entities in text.
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The spaCy command to extract named entities involves using the function ________.
The spaCy command to extract named entities involves using the function ________.
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________ is a Python library widely used for NER and natural language processing.
________ is a Python library widely used for NER and natural language processing.
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The interdisciplinary field combining Computer Science and Computational Linguistics is known as ______.
The interdisciplinary field combining Computer Science and Computational Linguistics is known as ______.
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Common audio formats used in speech recognition include WAV, MP3, and ______.
Common audio formats used in speech recognition include WAV, MP3, and ______.
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One major challenge in speech recognition is the variability in ______.
One major challenge in speech recognition is the variability in ______.
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Python package used for offline recognition in speech processing is ______.
Python package used for offline recognition in speech processing is ______.
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Voice Assistants are commonly found in phones, smart devices, and ______.
Voice Assistants are commonly found in phones, smart devices, and ______.
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Speech is digitized using a microphone and an ______.
Speech is digitized using a microphone and an ______.
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One technique used in speech recognition involves ______ which helps in the analysis of speech signals.
One technique used in speech recognition involves ______ which helps in the analysis of speech signals.
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Enhanced collaboration tools like Google ______ are a trend in the future of speech recognition.
Enhanced collaboration tools like Google ______ are a trend in the future of speech recognition.
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Study Notes
Speech Recognition
- Interdisciplinary subfield combining computer science and computational linguistics
- Converts human speech into text
- Also known as automatic speech recognition (ASR) or speech-to-text
Trends in Speech Recognition
- Replacing chat-based AI interfaces with voice input
- Improved AI-powered voice assistants
- Accessibility improvements (e.g., automatic captions)
- Enhanced collaboration tools (e.g., Google DuetAI)
Speech Recognition Applications
- Voice assistants (phones, smart devices, cars)
- Speech-to-text tools (automated meeting transcription)
- Accessibility tools for people with disabilities
- Security (speaker recognition for authentication)
How Speech Recognition Works
- Speech is digitized using a microphone and an analog-to-digital converter
- Core techniques include neural networks, hidden Markov models (HMMs), and voice activity detectors (VADs)
- Speech signals are analyzed at 10-millisecond intervals to generate cepstral coefficients (vectors representing signal features)
Challenges in Speech Recognition
- Variations in pronunciation (dialects, accents)
- Homophones ("bear" vs. "bare")
- Impact of noise and emotion
- Difficulty in identifying pauses or prosody
Speech Data and Formats
- Common audio formats: WAV, MP3, M4A, WMA
- Telephony systems use 8 kHz sampling rate
- Human hearing range: 20 Hz–20,000 Hz
Speech Analysis Applications
- Speaker diarization
- Emotional classification
- Text-to-speech (generating natural-sounding speech)
Python Packages for Speech Recognition
- SpeechRecognition (Google Web Speech API wrapper)
- Pocketsphinx (offline recognition)
- Other APIs (Google Cloud Speech, IBM Speech to Text, Whisper (OpenAI))
Self-Exercise and Implementation
- Record sentences as .wav files
- Use Python libraries (e.g., SpeechRecognition) to recognize speech
- Measure transcription accuracy
Statistical Parsing
- Probabilistic Context-Free Grammar (PCFG)
- Treebanks (corpora annotated with parse trees)
- Treebanks for supervised learning of PCFGs
- Parsing Techniques with PCFG (use of NLTK libraries such as InsideChartParser, ViterbiParser)
- Probabilistic parsing: defines grammar, generates parse trees, calculates probabilities
- Evaluation Metrics (recall, precision, F1-score in PARSEVAL)
- Dependency Grammar (PSG): Represents syntactic structure through dependencies rather than phrases
- Directed graphs between words, suitable for free word-order languages
Syntactic Parsing
- Phrase Structure Grammar (PSG): Introduced by Noam Chomsky, using rewrite rules
- Parsing as Search: Exploring all derivations for a given string
- Top-Down Parsing: Starts with the root (start symbol)
- Bottom-Up Parsing: Starts with terminal symbols, moving towards the root
Sentiment Analysis
- Focuses on analyzing opinions, sentiments, and emotions in text
- Uses NLP, statistics, and machine learning
- Sentiment analysis known also as opinion mining
- Key concepts include semantic orientation, polarity (e.g., positive, negative, or neutral)
- Subjective impressions influenced by contextual polarity
Levels of Sentiment Analysis
- Document level analyses overall sentiment
- Sentence level identifies sentiment for each sentence
- Entity/aspect level details sentiments concerning specific details (e.g., features of a product)
Challenges in Sentiment Analysis
- Complexity of opinions in text
- Issues like negation, sarcasm, and rhetorical devices
Steps in Sentiment Analysis using NLTK
- Training classifier models on labeled data
- Feature Extraction (e.g., Bag of Words model) to classify sentiments
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Description
This quiz tests your knowledge on sentiment analysis and related natural language processing (NLP) concepts. You will encounter questions about the levels of sentiment analysis, challenges faced, and various tagging techniques used in NLP. Enhance your understanding of how emotions are classified in text.
