Questions and Answers
What is add-one smoothing?
Adding 1 to all bigram counts and V (number of unique words in the corpus) to all unigram counts.
What does V represent in the context of smoothing?
The number of unique words in the corpus.
What are n-grams?
Contiguous sequences of n items generated from a sample of text.
What are the specific names for 1-grams, 2-grams, and 3-grams?
Signup and view all the answers
The purpose of smoothing is to improve the accuracy of language models.
Signup and view all the answers
Which of the following is an example of a smoothing technique?
Signup and view all the answers
What is the purpose of the predict_next_word function?
Signup and view all the answers
N-grams are useful for creating capabilities like autocorrect, ______, and text summarization.
Signup and view all the answers
What is the output of the everygrams function in NLTK?
Signup and view all the answers
What application does NOT involve language modeling?
Signup and view all the answers
How does backoff smoothing work?
Signup and view all the answers
What is the purpose of n-grams in language models?
Signup and view all the answers
What are the different types of n-grams mentioned?
Signup and view all the answers
Unigram language models consider the context of previous words.
Signup and view all the answers
What issue does Laplace smoothing address?
Signup and view all the answers
How does the unigram model estimate the probability of a word?
Signup and view all the answers
Which of the following language models analyzes text bidirectionally?
Signup and view all the answers
What does the average log likelihood metric indicate for a language model?
Signup and view all the answers
Bidirectional models only analyze text in one direction.
Signup and view all the answers
What happens to the probabilities of common tokens due to Laplace smoothing?
Signup and view all the answers
How is the probability of a sentence calculated in a language model?
Signup and view all the answers
What does NLP stand for?
Signup and view all the answers
Which three parts is the field of NLP divided into?
Signup and view all the answers
What is the main purpose of NLP?
Signup and view all the answers
Natural Language Processing is only used for text analysis.
Signup and view all the answers
Which model is commonly used in voice recognition systems?
Signup and view all the answers
What is Part-of-Speech tagging (POS)?
Signup and view all the answers
Which of the following is NOT a library used in NLP?
Signup and view all the answers
What is the purpose of spam filters in NLP?
Signup and view all the answers
Natural Language Processing is a combination of computer science, linguistics, and __________.
Signup and view all the answers
Match the following NLP libraries with their primary use:
Signup and view all the answers
What does NLG stand for in the context of NLP?
Signup and view all the answers
Study Notes
Overview of Natural Language Processing (NLP)
- NLP is a subfield of Artificial Intelligence focused on the interaction between computers and human language.
- It enables machines to understand, interpret, and generate human language for effective communication.
- NLP's growing demand is fueled by applications like personal assistants across various business sectors.
Components of NLP
- Speech Recognition: Converts spoken language into text using Hidden Markov Models (HMMs) for phoneme recognition.
- Natural Language Understanding (NLU): Comprehends the meaning of words and their grammatical context, using techniques like Part-of-Speech (POS) tagging.
- Natural Language Generation (NLG): Converts structured data into natural language, including text-to-speech capabilities.
Key Applications of NLP
- Machine Translation: Facilitates translation between languages.
- Chatterbots: AI systems that communicate with humans or other bots via text or voice.
- Spam Filters: Identifies and filters out unwanted emails through textual analysis.
- Algorithmic Trading: Analyzes market news to inform stock trading decisions.
- Summarization: Condenses long documents into concise summaries for easier understanding.
Future Trends in NLP
- Chatbots: Aid customer inquiries and provide resource information using NLP for communication.
- Invisible UI: Develops seamless human-machine interactions relying on natural language.
- Smarter Search Functionality: Enhances search engines to understand conversational queries instead of mere keywords.
Advances in NLP Technology
- Companies are exploring Deep Neural Networks (DNNs) to improve human-computer interaction quality.
- Expansion of language coverage, particularly for regional dialects and underrepresented languages.
Benefits of NLP in Business
- Empowers non-experts to extract valuable insights from complex datasets.
- Analyzes both structured and unstructured data for problem-solving and opportunity identification.
- Addresses fraudulent activities through pattern recognition in communication.
Use Cases by Industry
- Banking and Finance: Sentiment analysis for risk assessment and fraud detection.
- Insurance: Fraud claim identification through customer analysis.
- Manufacturing: Automation and efficiency improvement through process analysis.
- Retail: Enhancement of customer experience by analyzing product sentiment.
NLP Libraries and Tools
- NLTK: Provides extensive text processing tools and interfaces for various NLP tasks.
- Gensim: Focuses on topic modeling and document similarity retrieval for large datasets.
- CoreNLP: Java-based tool for linguistic analysis featuring named entity recognition and more.
- spaCy: Designed for production use with advanced NLP capabilities across multiple languages.
- TextBlob: Simplifies common text processing tasks through an accessible API.
- Pattern: Integrates web mining and data analysis features with NLP tools.
- PyNLPl: A collection of custom modules for both basic and advanced NLP tasks.
Language Modelling
- Language models convert qualitative language data into quantitative statistics for processing.
- N-grams, including unigrams, bigrams, and trigrams, are essential for defining word sequences and probabilities in language models.
- Unigram: Treats each word independently, foundational for information retrieval models.
Conclusion
NLP stands at the intersection of AI and linguistics, driving transformative changes in how humans interact with technology through sophisticated, language-driven applications. Its continuous evolution promises to enhance efficiency and enrich the user experience across various sectors.### Language Model Training
- Language models estimate word probabilities based on preceding words.
- Unigram models operate under the assumption of independence; word probabilities depend solely on their frequency in training text.
- The [END] token signals the end of a sentence, aiding in probability assignment.
Evaluating the Model
- Probability of sentences is calculated using the product of word probabilities, assuming sentence independence.
- The log likelihood metric is derived by summing log probabilities of words, averaged over the total word count.
- Common evaluation metrics include average log likelihood, cross-entropy (negative of log likelihood), and perplexity (exponential of cross-entropy).
Handling Unknown Unigrams
- Unigrams not present in training text lead to zero probabilities during evaluation, creating negative infinity in log likelihood.
- Introducing Laplace smoothing alleviates this by adding an artificial [UNK] token to account for unknown words.
Laplace Smoothing Technique
- Adds a pseudo-count to each unigram to avoid zero probabilities: typically, add-one smoothing is used.
- Alters word probabilities, redistributing some from frequent to infrequent words, leveling the probability distribution.
Bidirectional and Exponential Models
- Bidirectional models analyze text forwards and backwards, enhancing prediction accuracy.
- Exponential models leverage feature functions and n-grams, focusing on maximizing cross entropy for greater reliability.
Continuous Space Models
- Represent words using non-linear combinations of weights in neural networks, or word embeddings.
- Effective in large datasets with many unique words, mitigating the challenges of n-gram models in capturing word relationships.
Preprocessing Raw Text
- Text needs cleaning: removing newlines, special characters, and stopwords (common words with little meaning).
- NLTK library is used for tokenization and stopword removal.
Creating n-gram Language Models
- n-grams are sequences of n consecutive words: unigrams (1), bigrams (2), trigrams (3).
- Each n-gram's frequency is obtained using a frequency distribution function.
Next Word Prediction
- Probability of a sentence utilizes the chain rule; e.g., for "I love dogs":
- P(I love dogs) = P(I) * P(love | I) * P(dogs | I love).
- Bigram model predicts the next word using the previous word, while trigram uses the previous two words.
- Smoothing techniques (like Laplace smoothing) prevent zero probabilities in unseen bigrams or trigrams.
Implementation and Prediction
- Code involves tokenizing sentences, predicting the next word, and obtaining the next three words based on adjusted probabilities.
- The prediction process involves computing probabilities from smoothed bigrams or trigrams based on trained frequency distributions.
Example Test Sentences for Prediction
- "There was a sudden jerk, a terrific convulsion of the limbs; and there he"
- "They made room for the stranger, but he sat down"
- "The hungry and destitute situation of the infant orphan was duly reported by"
Summary of Predictions
- Predictions are generated using smoothed models to suggest the most likely next words, iterating through possible sequences.### N-gram Model in Natural Language Processing
- N-gram represents a contiguous sequence of n items (words or characters) from a text sample.
- Common N-gram types include Unigrams (1-grams), Bigrams (2-grams), and Trigrams (3-grams).
- Example: From "Either my way or no way," generate various N-grams depending on the value of n.
Use of N-grams
- N-grams enhance machine learning tasks by providing features for algorithms like SVM and Naive Bayes.
- Applications include text summarization, speech recognition, autocorrect, and autocompletion tools.
Generating N-grams in NLTK
- Utilize
ngramsfunction fromnltk.utilto create N-grams in Python. - Unigrams: Only single words are generated; e.g., "You will face many defeats" produces individual tuples for each word.
- Bigrams: Pairs of adjacent words are formed; e.g., “The purpose of our life” generates tuples like ('The', 'purpose').
- Trigrams: Triplets of words; e.g., "Whoever is happy" generates tuples like ('Whoever', 'is', 'happy').
- Generic N-gram function allows flexible input for sequence generation based on any n value.
Everygram in NLTK
-
everygramsfunction generates all possible N-grams from unigrams to N-grams based on the length of the input sentence.
Converting DataFrames into Trigrams
- Use the NLTK
ngramsfunction to convert text data in DataFrames into specified N-grams.
Smoothing in Language Modeling
- Smoothing techniques adjust probabilities in NLP models, improving predictions on unseen data and handling out-of-vocabulary words.
Types of Smoothing Techniques
- Laplace / Add-1 Smoothing: Adds 1 to all counts to avoid zero probabilities.
- Additive Smoothing: Similar to Laplace but adds a δ value.
- Backoff and Interpolation: Backoff checks lower order N-grams for alternatives; interpolation mixes several N-gram models.
- Good Turing Smoothing: Adjusts probabilities based on N-gram frequency distribution.
- Kneser-Ney Smoothing: Discounts observed N-grams, redistributing the probability to unseen ones.
- Katz Smoothing: Combines Good Turing with interpolation methods.
- Church and Gale Smoothing: Incorporates bucketing and Good Turing estimates for processed N-grams.
Applications of Language Modeling
- Speech Recognition: Recognizes and processes spoken words (e.g., Siri, Alexa).
- Machine Translation: Translates text between languages (e.g., Google Translate).
- Parts-of-Speech Tagging: Categorizes words grammatically (e.g., Brown Corpus).
- Parsing: Analyzes sentences according to grammar and syntax.
- Sentiment Analysis: Evaluates opinions expressed in texts, useful for businesses analyzing reviews or surveys.
- Optical Character Recognition (OCR): Converts images of text into machine-readable formats.
- Information Retrieval: Facilitates searching for information or documents within datasets and web browsers.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
This quiz covers the basics of Natural Language Processing (NLP) and various language modeling techniques, including Unigram, Bigram, Trigram, and N-gram models. It also discusses advanced smoothing methods and practical applications of language modeling in real-world scenarios.
