NLP Interview Questions and Answers- Part 5

Answer:

The Markov assumption is a key assumption made in the bigram language model. It is a probabilistic model used to predict the probability of a word given its previous word in a sequence of words. The bigram model is a simple language model that considers the probability of a word only based on the immediately preceding word, rather than taking into account the entire history of the sequence.

Answer:

You can you use many libraries for NLP in Python such as Scikit-learn, CoreNLP, GenSim, SpaCy, TextBlob, etc.

Answer:

Orthographic rules and morphological rules are both important aspects of language, particularly in the study of linguistics and language structure. They play distinct roles in governing how words are written and how they form different grammatical structures. Let’s differentiate between the two:

• Orthographic Rules: Orthography refers to the conventional spelling system of a language. Orthographic rules govern how words are spelled and how the written symbols (letters or characters) are used to represent sounds and meanings. These rules are concerned with the correct arrangement of letters and punctuation marks to convey meaning and facilitate communication.
• Morphological Rules: Morphology is the study of the structure and formation of words, including the morphemes, which are the smallest units of meaning in a language. Morphological rules govern how these morphemes combine to create words and how they change to convey different grammatical meanings.

In summary, orthographic rules deal with the correct spelling and writing conventions of a language, while morphological rules focus on the structure and formation of words, including how morphemes combine to create different words with specific meanings and grammatical functions. Both sets of rules are essential in understanding and using language effectively.

Answer:

Resolving an NLP issue requires a systematic approach and a combination of different actions. Here is a general outline of steps to address NLP problems:

1. Clearly define the NLP problem you are trying to solve.
2. Gather relevant and high-quality data for the specific NLP task. Preprocess the data by cleaning, tokenizing, removing stop words, handling special characters, and converting text to a suitable format for NLP models.
3. Conduct EDA to gain insights into the data distribution, class imbalances, and potential issues that may arise during modeling.
4. Choose an appropriate NLP model based on the task and data available.
5. Train the selected model on the preprocessed data using appropriate loss functions and optimization techniques.
6. Optimize the model’s hyperparameters to improve its performance and generalization.
7. Analyze the model’s errors to identify patterns and gain insights into areas where the model might be struggling.
8. If the dataset is limited, consider data augmentation techniques to increase its size and diversity.
9. For certain tasks, transfer learning from pre-trained models (e.g., BERT, GPT) can be highly effective, especially when the target task has limited data.
10. Fine-tune the pre-trained model on your specific task to adapt it to your dataset.
11. Combine multiple models through ensemble methods to improve overall performance.
12. If model interpretability is important, employ techniques such as attention visualization, saliency maps, or feature importance analysis.
13. Deploy the NLP model in a production environment and continuously monitor its performance and outputs.
14. NLP is an iterative process. Continuously evaluate the model’s performance, gather user feedback, and make incremental improvements as necessary.
15. Keep up with the latest advancements in NLP research and technology to incorporate new techniques and tools for better results.

Answer:

The Natural Language Toolkit, commonly known as NLTK, is a powerful open-source Python library designed to facilitate natural language processing (NLP) tasks. It provides a comprehensive set of tools and resources to work with human language data, making it an essential tool for researchers, students, and developers in the field of NLP.

Answer:

NLTK offers a wide range of functionalities, including:

1. Tokenization: Breaking text into individual words or sentences.
2. Part-of-speech tagging: Labeling each word with its grammatical category
3. Named entity recognition (NER): Identifying entities such as names of people, organizations, and locations in the text.
4. Text classification: Categorizing text into predefined classes or categories.
5. Stemming and Lemmatization: Reducing words to their base or root forms.
6. Chunking and parsing: Analyzing the sentence structure and identifying phrases.
7. Concordance and collocation: Finding occurrences and co-occurrences of words in a corpus.
8. Sentiment analysis: Determining the sentiment (positive, negative, neutral) expressed in a piece of text.
9. WordNet integration: Access to WordNet, a lexical database that provides word definitions, synonyms, and hypernyms.

Answer:

Some of the popular open-source NLP tools are:

• js
• NLTK
• Retext
• SpaCy
• TextBlob
• Textacy
• Stanford NLP
• CogcompNLP

Answer:

There are several types of ambiguity that can occur in language, and here are some of the most common ones:

• Lexical Ambiguity: This type of ambiguity arises from multiple meanings of a single word. Words with multiple definitions or homonyms are prime examples of lexical ambiguity.
• Structural Ambiguity: Structural ambiguity occurs when the arrangement of words in a sentence allows for more than one interpretation. It can result from ambiguous syntax or sentence structure.
• Semantic Ambiguity: Semantic ambiguity is related to the multiple meanings of a phrase or sentence due to the ambiguity of words used, rather than their structural arrangement. It occurs when words have more than one possible interpretation in a specific context.

Answer:

Both “hapax” and “hapax legomenon” are terms used in linguistics and philology to describe the occurrence of words. While they are related, there is a distinction between the two:

1. Hapax: A hapax (plural: hapaxes) is a term used to refer to a word or a form that occurs only once within a given corpus, text, or body of writing. In other words, it’s a word that appears uniquely and has no other instances within the specific context being analyzed. Hapaxes can be found in various languages and texts and are often of particular interest to researchers and lexicographers due to their rarity and potential for shedding light on the linguistic history or authorship of a text.
2. Hapax Legomenon: A hapax legomenon is a specific type of hapax, which is slightly more common. It refers to a word or form that appears only twice within a given context. The term “hapax legomenon” comes from the Greek words “hapax” (meaning “once”) and “legomenon” (meaning “spoken” or “said”). So, while hapaxes occur once, hapax legomena occur twice.

Answer:

Here are a few common word embedding techniques:

1. Word2Vec
2. FastText
3. Transformer-XL
4. GloVe (Global Vectors for Word Representation)
5. ELMo (Embeddings from Language Models)
6. BERT (Bidirectional Encoder Representations from Transformers)
7. GPT (Generative Pre-trained Transformer) series.

Answer:

Building a text classification system involves several steps, from data preprocessing to model training and evaluation. Let’s check out the entire process in detail:

• Define the Problem and Dataset: Clearly define the categories or classes you want to classify your text into.
• Data Preprocessing: Clean and preprocess the text data to make it suitable for machine learning algorithms.
• Feature Engineering: Convert text into numerical features that can be used by machine learning models.
• Splitting the Dataset: Divide your dataset into training, validation, and testing sets.
• Selecting a Model: Choose a machine learning model suitable for text classification tasks.
• Model Training: Feed the preprocessed text data into the chosen model.
• Model Evaluation: Evaluate the trained model’s performance on the validation set or use techniques.
• Hyperparameter Tuning: Optimize the model’s hyperparameters to achieve better performance.
• Final Testing: Once you are satisfied with the model’s performance on the validation set, test it on the unseen testing set to get an estimate of its real-world performance.
• Deployment: Once the model meets the desired performance level, deploy it in your desired application or system to make predictions on new, unseen text data.

Answer:

Autoencoders are a type of artificial neural network used in unsupervised learning tasks. They are designed to encode data into a compressed representation and then decode it back to its original form. The primary goal of autoencoders is to learn an efficient and compact representation of the input data in an unsupervised manner.

Answer:

Functions and Applications of autoencoders are:

1. Dimensionality Reduction: Autoencoders can be used for dimensionality reduction, where they learn a compact representation of high-dimensional data. This is useful when dealing with large datasets, as it reduces computational complexity and storage requirements while preserving the most relevant information.
2. Data Compression: Autoencoders can compress data into a smaller representation, making it more suitable for storage or transmission. They find applications in image and video compression, where reducing file sizes without significant loss of quality is desired.
3. Feature Learning: Autoencoders can automatically learn meaningful features from the data. Once trained on unlabeled data, the encoder part of the autoencoder can be used as a feature extractor for other machine learning tasks.
4. Anomaly Detection: Autoencoders can be used for anomaly detection by reconstructing input data and measuring the reconstruction error. Unusual or anomalous samples often result in higher reconstruction errors, making it possible to identify outliers or anomalies in the data.
5. Denoising: Autoencoders can be trained to remove noise from data during the reconstruction process, making them useful for denoising images, audio, or other noisy signals.
6. Generative Models: Variations of autoencoders, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), can be used to generate new data samples that resemble the training data. These models have applications in generating images, audio, and other synthetic data.

Answer:

Feature engineering is the process of selecting, transforming, and creating relevant features from raw data to improve the performance of machine learning algorithms. In simple terms, it involves deriving new features or selecting existing ones to make the data more suitable for a particular machine learning task.

Answer:

The main goals of feature engineering are:

• Improving Model Performance
• Dimensionality Reduction
• Handling Missing Data
• Removing Noise
• Handling Categorical Data

Answer:

Some common techniques used in feature engineering include:

• One-Hot Encoding:Converting categorical variables into binary vectors to represent the presence or absence of a particular category.
• Scaling and Normalization:Scaling numerical features to ensure they have similar ranges and distributions, preventing one feature from dominating others.
• Polynomial Features:Creating higher-order features by taking powers or interaction terms of existing features.
• Handling Dates and Times:Extracting relevant information from date and time variables, such as day of the week or month, which might be useful in certain applications.
• Binning/Discretization:Grouping continuous data into bins or discrete categories to simplify the representation or capture nonlinear relationships.
• Logarithmic Transform:Applying logarithmic transformations to skewed numerical features to make their distribution more Gaussian-like.
• Feature Selection:Selecting the most relevant features based on their importance or correlation with the target variable.

Answer:

In NLP, a text corpus refers to a large and structured collection of textual data. It serves as the primary data source for training, validating, and testing various NLP models and algorithms. A text corpus can contain diverse texts, such as books, articles, websites, social media posts, emails, and more, encompassing different languages, genres, and topics.

Answer:

Here are some key features of a text corpus:

1. Size: Corpora can vary greatly in size, from small ones with a few thousand documents to massive ones with millions or even billions of text units.
2. Composition: A corpus can be composed of various types of text, such as books, articles, web pages, emails, tweets, forum posts, scientific papers, etc.
3. Text Units: Corpora consist of individual text units, which could be sentences, paragraphs, articles, or entire documents.
4. Text Type: Corpora can be specialized to focus on specific topics or genres like legal texts, medical documents, news articles, literary works, etc.
5. Annotation: Some corpora may include annotations like part-of-speech tags, named entities, sentiment labels, or other linguistic annotations to aid in specific NLP tasks.
6. Multilingual or Monolingual: Depending on the scope, a corpus can be monolingual, containing text from a single language, or multilingual, comprising texts from multiple languages.
7. Representativeness: A good corpus should be representative of the language or domain it aims to cover. It should include a diverse range of topics, genres, and writing styles.
8. Availability: Corpora can be publicly available or proprietary, depending on how they are compiled and distributed.

Answer:

Stopwords are common words that appear frequently in a language but carry little meaning. In NLP, Stopwords are often removed during text preprocessing to reduce noise and improve computational efficiency.

Answer:

Seq2Seq (Sequence-to-Sequence) models consist of an encoder and a decoder. The encoder processes the input sequence and creates a fixed-size context vector, which is then fed into the decoder to generate the output sequence. Seq2Seq models are commonly used in tasks like machine translation and chatbot development.