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Questions and Answers
Text summarization generates concise versions of large texts without losing essential ______.
Text summarization generates concise versions of large texts without losing essential ______.
information
Extractive Summarization selects key sentences or phrases directly from the ______.
Extractive Summarization selects key sentences or phrases directly from the ______.
source
The Seq2Seq Model with Attention improves performance by addressing the 'information ______'.
The Seq2Seq Model with Attention improves performance by addressing the 'information ______'.
bottleneck
Transformers utilize self-______ for higher-quality summaries.
Transformers utilize self-______ for higher-quality summaries.
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Word embeddings represent words as dense vectors in low-dimensional ______.
Word embeddings represent words as dense vectors in low-dimensional ______.
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Dense embeddings solve the issues of sparsity and dimensionality found in 'one-hot' ______.
Dense embeddings solve the issues of sparsity and dimensionality found in 'one-hot' ______.
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LLaMA is a large language model trained on trillions of ______ in multiple languages.
LLaMA is a large language model trained on trillions of ______ in multiple languages.
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GloVe combines global co-occurrence statistics with vector ______.
GloVe combines global co-occurrence statistics with vector ______.
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Text summarization creates __________ versions of texts for quicker consumption.
Text summarization creates __________ versions of texts for quicker consumption.
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Extractive summarization involves selecting __________ from the original text.
Extractive summarization involves selecting __________ from the original text.
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Abstractive summarization uses deep learning models like __________ or __________.
Abstractive summarization uses deep learning models like __________ or __________.
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The __________ model uses attention mechanisms to avoid information bottlenecks.
The __________ model uses attention mechanisms to avoid information bottlenecks.
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__________ is a library that implements summarization algorithms like TextRank and LSA.
__________ is a library that implements summarization algorithms like TextRank and LSA.
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Word embeddings represent words as __________ vectors.
Word embeddings represent words as __________ vectors.
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The transformer mechanism introduces __________ for high-quality outputs.
The transformer mechanism introduces __________ for high-quality outputs.
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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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__________ is a foundational language model trained on trillions of tokens by Meta AI.
__________ is a foundational language model trained on trillions of tokens by Meta AI.
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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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In the Viterbi algorithm, probabilities are computed by taking the _______ over all possible paths leading to a state.
In the Viterbi algorithm, probabilities are computed by taking the _______ over all possible paths leading to a state.
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The HMM component that specifies the probability of starting in each state is called _______.
The HMM component that specifies the probability of starting in each state is called _______.
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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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The Forward and Viterbi algorithms both use _______ programming to improve computational efficiency.
The Forward and Viterbi algorithms both use _______ programming to improve computational efficiency.
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Statistical parsing uses probabilistic models to assign probabilities to _______ trees.
Statistical parsing uses probabilistic models to assign probabilities to _______ trees.
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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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Parsing techniques often use NLTK libraries for _______ parsing.
Parsing techniques often use NLTK libraries for _______ parsing.
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Evaluation metrics like PARSEVAL measure how well parse trees align with _______ standards.
Evaluation metrics like PARSEVAL measure how well parse trees align with _______ standards.
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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.
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The three levels of sentiment analysis are ________, ________, and ________.
The three levels of sentiment analysis are ________, ________, and ________.
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Challenges in sentiment analysis include complexity of opinions in text and issues like ________, sarcasm, and rhetorical devices.
Challenges in sentiment analysis include complexity of opinions in text and issues like ________, sarcasm, and rhetorical devices.
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In sentiment analysis using NLTK, a key step is to train classifiers with ________ data.
In sentiment analysis using NLTK, a key step is to train classifiers with ________ data.
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NER locates and classifies entities in text into categories like names, organizations, and ________.
NER locates and classifies entities in text into categories like names, organizations, and ________.
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The three types of NER systems include Dictionary-Based, Rule-Based, and ________.
The three types of NER systems include Dictionary-Based, Rule-Based, and ________.
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Techniques for NER implementation include tokenization, part-of-speech tagging, and ________ tagging.
Techniques for NER implementation include tokenization, part-of-speech tagging, and ________ tagging.
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The spaCy library is pre-trained on the ________ corpus, supporting multiple entity types.
The spaCy library is pre-trained on the ________ corpus, supporting multiple entity types.
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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 field that combines Computer Science and Computational Linguistics to convert human speech into text is known as ________.
The field that combines Computer Science and Computational Linguistics to convert human speech into text is known as ________.
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One trend in speech recognition is the replacement of chat-based AI interfaces with ________ input.
One trend in speech recognition is the replacement of chat-based AI interfaces with ________ input.
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Core techniques in speech recognition include Neural Networks and ________ Markov Models.
Core techniques in speech recognition include Neural Networks and ________ Markov Models.
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The common audio format used in telephony systems typically has a sampling rate of ________ kHz.
The common audio format used in telephony systems typically has a sampling rate of ________ kHz.
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In speech analysis, identifying 'who spoke when' is referred to as ________ Diarization.
In speech analysis, identifying 'who spoke when' is referred to as ________ Diarization.
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________ is one of the Python packages used for offline speech recognition.
________ is one of the Python packages used for offline speech recognition.
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To measure transcription accuracy, one can use Python libraries like SpeechRecognition to recognize ________.
To measure transcription accuracy, one can use Python libraries like SpeechRecognition to recognize ________.
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Study Notes
Text Summarization
- Text summarization condenses large texts without losing essential information.
- Common applications include news aggregators like Google News and Inshorts.
Types of Text Summarization
- Extractive Summarization: Selects key sentences or phrases directly from the source. Methods include frequency-based techniques (TF-IDF) and tools like Sumy.
- Abstractive Summarization: Generates summaries using deep learning models (e.g., BERT, GPT). This paraphrases content rather than copying phrases.
Deep Learning Methods
- Seq2Seq Model with Attention: Encoder (Bi-directional LSTM) extracts input features. Decoder (Uni-directional LSTM) generates summaries word-by-word. Attention Mechanism improves performance by handling information bottlenecks.
- Transformers: Utilize self-attention for higher-quality summaries. Examples include PEGASUS, pre-trained by masking key sentences and reconstructing them.
Algorithms and Tools
- Frequency Method: Selects sentences with high-frequency terms.
- Sumy Library: Implements various summarization algorithms.
- LSA (Latent Semantic Analysis): Projects data into a low-dimensional space while preserving semantics.
- LexRank (Cosine Similarity): Measures sentence similarity to create summaries.
Large Language Models (LLMs) & Word Embeddings
- Word Embeddings: Represent words as dense vectors in a low-dimensional space (e.g., 25-1000 dimensions).
- Examples: Word2Vec (predicts surrounding words), GloVe (combines global co-occurrence statistics with vector representations).
- Limitations of Traditional Representations: "One-hot" vectors are high-dimensional, sparse, and lack semantic meaning. Dense embeddings address these problems.
- Semantic Patterns: Word embeddings capture relationships (e.g., king – man ≈ queen – woman).
- Large Language Models (LLMs): LLAMA is trained on trillions of tokens in multiple languages with billions of parameters for improved text generation. Other notable examples include GPT, BERT, and Meta's LLaMA.
- Advantages: Pretrained word vectors improve downstream NLP tasks, and self-attention enhances understanding of context.
Fill-in-the-Blank Summary
- Text summarization: creates shorter versions of texts.
- Extractive summarization: involves selecting key sentences.
- Abstractive summarization: uses deep learning models like BERT or GPT.
- Seq2Seq model: uses attention mechanisms to avoid information bottlenecks.
- Sumy: is a library for summarization algorithms like LexRank.
- Word embeddings are represented as dense vectors.
- GloVe: combines global word-word co-occurrence.
- "One-hot" vectors: are high-dimensional and sparse.
- Transformer mechanisms: introduce self-attention and high-quality output.
- LLaMA: is a foundational language model trained on trillions of tokens.
Classification
- Machine Learning & NLP Integration: Machine learning learns relationships from features in data. Classification (supervised) predicts classes using labeled data; clustering (unsupervised) groups data without labels.
- Text Representation: Converts human-readable text into numbers for computational processing.
- Machine Learning Types: Supervised learning utilizes labeled data, unsupervised learning infers structure from unlabeled data, and semi-supervised learning combines small labeled data with larger unlabeled data.
- Applications: Examples include healthcare, inventory management, translation, and self-driving cars.
- Deep Learning and NLP: Learning representation through successive layers. Applications include transformers like Google BERT, word embeddings (Word2Vec, GloVe), and reinforcement learning for tasks like NLG
- Python Libraries: Key libraries include NumPy, SciPy, NLTK, Scikit-learn, Pandas, Matplotlib.
- Naïve Bayes Classification: Based on Bayes theorem, assumes feature independence, works well for categorical variables, requires feature extraction.
- Text Classification Example: ...(example provided in the document)
Clustering
- Text Clustering: Groups texts with similar characteristics. Useful for analyzing large, unstructured datasets.
- K-Means Clustering Algorithm/Steps: Finds groups in data with K representing the number of clusters.
- Initialization: Initial centroids (randomly or from data).
- Data Assignment: Assign data points to the nearest centroid (Euclidean distance).
- Updates: Centroids are updated iteratively.
- Visual Representation: Clustering with terms can be visualized using 2D scatterplots with cosine distance.
- Pre-processing: Stops word removal, Normalization (to lowercase, removing punctuations), Tokenization (splitting text and counting occurrences), Stemming (simplifying word forms).
- Vectorization and tf-idf: converts text-to-numbers using TfIdfVectorizer, it assigns importance scores.
- Implementation: Implements tokenization and stemming, and creates a matrix where rows represent files and columns represent terms with tf-idf scores.
Part-of-Speech (POS) Tagging
- POS Tagging: Assigns lexical class markers/tags to words in a corpus. Useful for speech recognition, word sense disambiguation, and other NLP tasks.
- Word Classes: Closed classes (fixed sets) include prepositions and conjunctions; open classes (expanding) include nouns, verbs, and adjectives.
- Tagsets: Penn Treebank Tagset (45 tags) and C5 Tagset (61 tags).
- Ambiguities: Words (like "book" or"like") can have multiple POS tags depending on context.
- Approaches: Rule-based (handcrafted rules); Learning-based (corpora and machine learning: Naive Bayes, Neural Networks, HMMs).
- Probabilistic Models: HMMs assume the next state depends only on the current; Conditional Random Fields (CRFs) consider global dependencies for sequence labeling.
- Training and Evaluation: Training phase estimates word-tag and tag transition probabilities. Evaluation metrics like precision, recall, and F-measure assess performance.
- Sequence Labeling Problem: Classifies each token in a sequence considering dependencies between neighboring tokens
Statistical Parsing
- Overview: Statistical parsing uses probabilistic models/assign probabilities to parse trees. This helps resolve syntactic ambiguity and allows supervised/unsupervised parser learning.
- Probabilistic Context-Free Grammars (PCFGs): A CFG variant where each production rule has an associated probability defining non-terminal distributions.
- Treebanks: Annotated corpora with parse trees like Penn Treebank, these provide foundations for supervised learning.
- Parsing Techniques: Use of NLTK libraries for parsing; steps include defining grammar, generating parse trees, calculating probabilities
- Evaluation Metrics: PARSEVAL metrics (Recall, Precision, F1-score) measure parse trees' alignment with gold standards.
Syntactic Parsing
- Phrase Structure Grammar (PSG): Introduced by Noam Chomsky, sentences are generated using rewrite rules. This focuses on deriving correct syntax trees of sentences .
- Parsing as Search: Explores all derivations to derive a given string: Top-down (from the root) and bottom-up (starting with terminal symbols).
- Parsing Strategies: Top-down exploring inconsistent options early and may generate invalid trees; bottom-up avoiding inconsistencies but might not reach complete parses.
Fill-in-the-Blank Questions
- (Answers provided in the document)
Text Analytics & Sentiment Analysis
- Sentiment Analysis: Analyzes opinions, sentiments, and emotions in text. Uses NLP, statistics, and machine learning. Also known as opinion mining.
- Sentiment Analysis Concepts: Semantic Orientation/Polarity (positive, negative, neutral); Subjective Impressions (based on personal judgements, emotional state).
- Levels: Document level (overall sentiment), sentence level, entity/aspect level (specific details, ex: opinions on product features).
- Challenges: Text Complexity, Negations, Sarcasm, Rhetorical Devices.
- NER: Locates and classifies entities in text (names, organizations, locations).
- Evaluation Technique Examples: Sentiment lexicons, and Pointwise Mutual Information (PMI)
Speech Recognition
- Speech Recognition: Converts human speech to text using algorithms and technologies.. Often referred to as Automatic Speech Recognition (ASR).
- Speech Recognition Trends: Replacing chat-based AI interfaces with voice input, improved AI-powered voice assistants, and accessibility improvements (auto-captions).
- How it Works: Speech is digitized, neural networks, hidden Markov models (HMM), and voice activity detectors (VADs) analyze 10-millisecond intervals for cepstral coefficients (signal vectors), and compute probabilities of sentences..
- Challenges: Variability in pronunciation (e.g., accents), homophones, noise/emotional impact, difficulty in identifying pauses/prosody.
- Data/Formats: WAV, MP3, M4A, WMA audio; telephony sampling rate: 8kHz.
- Applications: Voice assistants, speech-to-text tools, accessibility, and security (speaker recognition).
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Description
Test your knowledge on text summarization techniques, including extractive and abstractive methods. This quiz covers crucial models and concepts like Seq2Seq, Transformers, and word embeddings that enhance summary quality. Challenge yourself to identify key elements and understand their applications in natural language processing.
