Text Analysis 101 Cognitive Class Exam Quiz Answers

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Enroll Here: Text Analysis 101 Cognitive Class Exam Quiz Answers

Text Analysis 101 Cognitive Class Certification Answers

Module 1 – Getting to know IE Quiz Answers – Cognitive Class

Question 1: Your client would like to know how its advertising campaign impressed customers. Which IE task would provide this data?

• Event Extraction
• Co-reference resolution
• Sentiment Extraction
• Relation Extraction

Question 2: Which extraction phase can turn a dictionary match of a common first name plus an adjacent regular expression into a “potential person name” entity?

• None of these
• Entity Resolution
• Named Entity Recognition
• Feature Selection

Question 3: Consider a set of news articles that contains 20 mentions of person names. From this source, an extractor extracts 15 entities, 3 of which are incorrect. What are the Precision (P) and Recall (R) values?

• P = 0.30, R = 0.70
• P = 0.70, R = 0.30
• P = 0.80, R = 0.60
• P = 1.00, R = 1.30

Module 2 – Limitations in IE Quiz Answers – Cognitive Class

Question 1: Which of the following poses huge demands on the IE engine?

• Complex IE tasks
• Heterogeneous text inputs
• Different types of data
• All of the above

Question 2: A typical IE grammar-based workflow:

• Targets the step closest to the nature of the rule applied.
• Follows a unidirectional sequence of steps.
• Targets each step based on the nature of the input data.
• Creates a branched path according to the input and the desired output.

Question 3: We can overcome IE performance limitations by:

• Separating extractor semantics from execution strategy.
• Coupling extractor semantics with execution strategy.
• Parallel processing.
• Making faster finite state transducers.

Module 3 – Getting to know System T Quiz Answers – Cognitive Class

Question 1: What outputs do extractors generate in System T?

• Extractors
• Regular Expressions
• Annotations
• None of above

Question 2: An output refiner helps you to:

• Define multiple filters.
• Union multiple extractors.
• Define multiple extractors.
• None of above

Question 3: By selecting the Mapping Table checkbox in the Dictionary extractor, you can:

• Map dictionary terms against categories.
• Create a two-column dictionary.
• Add a column of metadata.
• All of the above.

Module 4 – IE with AQL Quiz Answers – Cognitive Class

Question 1: Which of the following statements describes AQL?

• AQL has a syntax that is similar to SQL.
• AQL has expressive power of algebra.
• AQL separates semantics from implementation.
• All of the above.

Question 2: What are the main advantages of SystemT’s approach towards Information Extraction?

• Richer and cleaner rule semantics
• Better performance through optimization
• Improved quality of results
• A and B

Question 3: Which of the following files can be part of an AQL module?

• Dictionary file
• AQL file
• UDF jar
• All of the above

Module 5 – AQL Basics Quiz Answers – Cognitive Class

Question 1: Which factor is essential for the Union All statement to work?

• The tuples should be from the same input text.
• The schemas of the tuples should be different.
• The schemas of the tuples should be from a single view.
• The schemas of the tuples should be same.

Question 2: Which of the following options is a valid consolidate policy?

• ContainsButNotEqual
• RightToLeft
• ExactEqual
• ContainedInside

Question 3: When is the Minus statement useful?

• When the two sets of input tuples have different schemas
• When you want to find matches for a sequence pattern
• When you want to subtract a set of tuples from another set of tuples
• All of the above

Module 6 – Advanced AQL Quiz Answers – Cognitive Class

Question 1: Which type of text can be extracted using the Detag statement?

• Semi-structured text
• Unstructured text
• Structured text
• None of above

Question 2: When should you use a standard tokenizer?

• When token boundaries are defined by punctuation and whitespace.
• When extraction of person names from Chinese text is needed.
• When extraction of parts of speech is required.
• All of the above.

Question 3: Which best practices should you use when developing an AQL module?

• Place large dictionaries and tables in separate modules.
• Avoid using the output view statement when developing extractor libraries.
• Document the source code using AQL Doc.
• All of the above.

Module 7 – Declarative IE and the System T optimizer Quiz Answers – Cognitive Class

Question 1: Which of the following leads to mistakes when two rules match the same region of text?

• Limited expressivity
• Lossy sequencing
• Rigid matching priority
• None of the above

Question 2: Which of the following strategies can overcome lossy sequencing?

• Expand rule patterns to include features such as aggregation.
• Impose modular tokenization.
• Include matching regimes that increase flexibility on priority.
• Use grammar rules that operate on graphs rather than sequences of annotations.

Question 3: In which stage of the SystemT optimizer do you merge block plans into a single operator graph?

• Post-processor
• Planner
• Pre-processor
• None of the above

Module 8 – Best Pratices Quiz Answers – Cognitive Class

Question 1: Why is it that the first document in a collection is often at the top of the AQL Profiler’s “hot” document’s view?

• The optimizer is trying to produce plans that are sensitive to each input document.
• This is because of how Java implements regex.
• This is due to the Java compiler.
• System T sorts documents by length for processing, so the first document is the longest.

Question 2: Which of the following is NOT a best practice for writing AQL?

• Use the AQL profiler to find and address hot spots.
• Follow simple rules of thumb when writing AQL.
• Don’t hand-tune while writing AQL.
• Always ignore throughput levels when designing extractors.

Question 3: Why is it necessary to be selective about performance tuning?

• It might adversely affect code readability
• It might reduce the quality of your results
• It might make your code more difficult to maintain
• A and B.
• A and C.

Text Analytics 101 Final Exam Answers – Cognitive Class

Question 1: Identify the logical sequence of phases in an IE system.

• Entity Identification > Feature Selection > Entity Resolution
• Entity Identification > Entity Resolution > Feature Selection
• Feature Selection > Entity Resolution > Entity Identification
• Feature Selection > Entity Identification > Entity Resolution

Question 2: Consider a set of news articles that contains 100 mentions of organizations. From this source, an extractor extracts 75 entities, 50 of which are correct. What are the Precision (P) and Recall (R) values of this extractor?

• P = 0.75, R = 0.50
• P = 0.67, R = 1.50
• P = 0.67, R = 0.50
• P = 0.50, R = 0.67

Question 3: What problem is caused by an IE system having a rigid matching priority?

• Regular expressions cannot be used when specifying rules.
• There is no support for matching strings spanning more than one token.
• The system cannot express aggregation operations.
• When multiple rules match the same region of text, mistakes are likely to occur.

Question 4: The System T consolidate policy:

• Applies a filtering predicate to output tuples.
• Specified how to handle tuples with overlapping spans.
• Specifies which tuple columns to group on.
• Specifies a tuple ordering.

Question 5: Which of the following AQL statements uses expressions, dictionaries, and sequence patterns to perform extraction?

• Relational style statement.
• Extract statement.
• Create table statement.
• Select statement.

Question 6: Which of the following statements are part of an AQL file?

• Create external table statements.
• Import statements.
• Create external dictionary statements.
• Export statements.
• All of the above.

Question 7: Which of the following types is a return value for table UDFs?

• Tuples.
• Integer.
• Span.
• Boolean.

Question 8: Which predicate would you use to check if a span is exactly equal to one of a predefined set of words?

• FollowsTok.
• MatchesRegex.
• MatchesDict.
• ContainsDict.

Question 9: Why is correct text tokenization important?

• Dictionary evaluation and many extraction operators, such as regex, are done on token boundaries, and incorrect tokenization will lead to incorrect results.
• Several built-in predicates and functions are token sensitive.
• AQL extract statements will not compile if tokenization is incorrect.
• A and B.
• A and C.

Question 10: Which of the following is NOT a best practice rule of thumb to follow when writing AQL?

• Use dictionaries instead of regex whenever possible.
• Make sure each module has its own copy of every dictionary.
• Avoid using UDFs as join predicates.
• Avoid Cartesian products.

Introduction to Text Analysis 101

Text analysis is the process of deriving meaningful insights and information from written text. Whether you’re analyzing literature, social media data, customer feedback, or any other form of written communication, text analysis techniques can help you extract valuable knowledge and patterns.

Here’s a basic overview of the key concepts and techniques in text analysis:

1. Text Preprocessing: Before diving into analysis, it’s essential to preprocess the text. This involves steps like removing punctuation, converting text to lowercase, removing stop words (common words like “and,” “the,” “is”), and stemming or lemmatizing words (reducing them to their root form).
2. Tokenization: Tokenization involves breaking down text into smaller units, such as words, phrases, or sentences. This step is crucial for further analysis because it allows you to manipulate and analyze text at a more granular level.
3. Statistical Analysis: Statistical techniques such as frequency analysis, TF-IDF (Term Frequency-Inverse Document Frequency), and n-grams analysis help identify important words or phrases within a corpus of text. These methods can reveal patterns, trends, or anomalies in the data.
4. Sentiment Analysis: Sentiment analysis aims to determine the emotional tone of a piece of text. It can help businesses understand customer sentiment toward their products or services, or analyze public opinion on social media platforms.
5. Topic Modeling: Topic modeling is a statistical technique for uncovering hidden thematic structures within a collection of documents. Techniques like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) can identify topics and their prevalence in the text.
6. Named Entity Recognition (NER): NER identifies and classifies named entities mentioned in text, such as people, organizations, locations, dates, and more. It’s useful for tasks like information extraction and content categorization.
7. Text Classification: Text classification involves categorizing text documents into predefined categories or classes. Machine learning algorithms like Naive Bayes, Support Vector Machines (SVM), or deep learning models such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) are commonly used for this task.
8. Text Generation: Text generation involves creating new text based on existing text data. Techniques such as Markov chains, recurrent neural networks (RNNs), and transformer-based models like GPT (like me!) are used for generating coherent and contextually relevant text.
9. Evaluation: Evaluating the performance of text analysis models is crucial to ensure their effectiveness. Metrics such as accuracy, precision, recall, F1-score, and perplexity are commonly used to assess model performance.
10. Visualization: Visualizing text data can aid in understanding patterns and trends. Techniques like word clouds, bar charts, and heatmaps can be used to represent textual information in a visually appealing and informative manner.