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Questions and Answers
What is the main advantage of using Named Entity Recognition in customer support?
What is the main advantage of using Named Entity Recognition in customer support?
Which sector benefits from NER by improving understanding of reports and reducing workload for healthcare professionals?
Which sector benefits from NER by improving understanding of reports and reducing workload for healthcare professionals?
What distinguishes flat NER from nested NER?
What distinguishes flat NER from nested NER?
In what way does NER enhance search engines?
In what way does NER enhance search engines?
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Which of the following best describes the output of a Named Entity Recognition process?
Which of the following best describes the output of a Named Entity Recognition process?
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How does NER contribute to human resources?
How does NER contribute to human resources?
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Which application of NER is particularly crucial for managing large amounts of data generated on social media platforms?
Which application of NER is particularly crucial for managing large amounts of data generated on social media platforms?
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What is a potential limitation of flat NER approaches compared to nested NER?
What is a potential limitation of flat NER approaches compared to nested NER?
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What is the primary advantage of using deep learning approaches for Named Entity Recognition (NER)?
What is the primary advantage of using deep learning approaches for Named Entity Recognition (NER)?
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Which tag scheme is most commonly used in feature-based supervised learning approaches for NER?
Which tag scheme is most commonly used in feature-based supervised learning approaches for NER?
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Which of the following NER features is classified under document and corpus features?
Which of the following NER features is classified under document and corpus features?
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Which deep learning architecture was originally developed by Collobert and Weston for NER?
Which deep learning architecture was originally developed by Collobert and Weston for NER?
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Which of the following is NOT commonly used in supervised NER systems?
Which of the following is NOT commonly used in supervised NER systems?
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What is the typical flat NER task defined as in sequence tagging?
What is the typical flat NER task defined as in sequence tagging?
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Which machine learning algorithm has gained popularity for NER specifically in biomedical texts?
Which machine learning algorithm has gained popularity for NER specifically in biomedical texts?
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What is the role of pre-trained word-level embeddings in deep learning for NER?
What is the role of pre-trained word-level embeddings in deep learning for NER?
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What is a key difference between flat NER and nested NER?
What is a key difference between flat NER and nested NER?
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Which dataset specifically includes multiple languages and focuses on four entities: PER, LOC, ORG, and MISC?
Which dataset specifically includes multiple languages and focuses on four entities: PER, LOC, ORG, and MISC?
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What percentage of sentences in the ACE 2004 and ACE 2005 English datasets contain nested entities?
What percentage of sentences in the ACE 2004 and ACE 2005 English datasets contain nested entities?
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How many entity categories are present in the English NER data of the OntoNotes 5.0 project?
How many entity categories are present in the English NER data of the OntoNotes 5.0 project?
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Which dataset focuses on user-generated texts like tweets and YouTube comments?
Which dataset focuses on user-generated texts like tweets and YouTube comments?
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What types of entities does the NCBI disease corpus specifically focus on?
What types of entities does the NCBI disease corpus specifically focus on?
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What is the maximum nested depth that can be found in the ACE 2004 and ACE 2005 datasets?
What is the maximum nested depth that can be found in the ACE 2004 and ACE 2005 datasets?
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Which of the following datasets is domain-specific and relates to the field of medicine?
Which of the following datasets is domain-specific and relates to the field of medicine?
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Which of the following is NOT a characteristic of rule-based approaches to named entity recognition (NER)?
Which of the following is NOT a characteristic of rule-based approaches to named entity recognition (NER)?
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What is a primary drawback of rule-based approaches in NER?
What is a primary drawback of rule-based approaches in NER?
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What did Collins et al. (1999) contribute to unsupervised learning approaches?
What did Collins et al. (1999) contribute to unsupervised learning approaches?
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Which of the following best describes the unsupervised approach proposed by Zhang and Elhadad (2013)?
Which of the following best describes the unsupervised approach proposed by Zhang and Elhadad (2013)?
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What is a common technique used in feature-based supervised learning approaches for NER?
What is a common technique used in feature-based supervised learning approaches for NER?
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What factor negatively impacts the ability of rule-based systems in NER to transfer to other domains?
What factor negatively impacts the ability of rule-based systems in NER to transfer to other domains?
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What approach did Etzioni et al. (2005) utilize to enhance recall in NER systems?
What approach did Etzioni et al. (2005) utilize to enhance recall in NER systems?
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What is a significant benefit of unsupervised learning approaches in NER?
What is a significant benefit of unsupervised learning approaches in NER?
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Which type of neural network architecture is frequently used for context encoding in NER tasks?
Which type of neural network architecture is frequently used for context encoding in NER tasks?
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What is the primary benefit of using character-level representations in NER?
What is the primary benefit of using character-level representations in NER?
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Which component is considered the final stage in the NER deep learning architecture?
Which component is considered the final stage in the NER deep learning architecture?
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Which hybrid representation element assists in providing additional insights during the input phase?
Which hybrid representation element assists in providing additional insights during the input phase?
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In what manner do conditional random fields (CRFs) improve the tagging process in NER?
In what manner do conditional random fields (CRFs) improve the tagging process in NER?
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Which deep learning model can be directly utilized in NER tasks for context encoding?
Which deep learning model can be directly utilized in NER tasks for context encoding?
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What role does the MLP + softmax layer play in the NER architecture?
What role does the MLP + softmax layer play in the NER architecture?
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What is a notable feature of RNNs compared to CRFs when decoding tags for NER tasks?
What is a notable feature of RNNs compared to CRFs when decoding tags for NER tasks?
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Which of the following statements about the GENIA corpus is true?
Which of the following statements about the GENIA corpus is true?
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What characterizes the AnCora dataset?
What characterizes the AnCora dataset?
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What does traditional Precision, Recall, and F-score in NER evaluation assess?
What does traditional Precision, Recall, and F-score in NER evaluation assess?
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In the context of NER evaluation metrics, what does Macro-averaged F-score measure?
In the context of NER evaluation metrics, what does Macro-averaged F-score measure?
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What distinguishes relaxed match evaluation from exact match evaluation in NER?
What distinguishes relaxed match evaluation from exact match evaluation in NER?
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Which statement about the NNE dataset is accurate?
Which statement about the NNE dataset is accurate?
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What is a key requirement for an entity to be scored as a True Positive in NER?
What is a key requirement for an entity to be scored as a True Positive in NER?
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How are entities nested in the NNE dataset described?
How are entities nested in the NNE dataset described?
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Study Notes
Text Mining: Information Extraction
- Information Extraction (IE) is the task of automatically extracting structured information from unstructured or semi-structured documents and other electronic sources.
- IE often uses Natural Language Processing (NLP) techniques.
- Typical IE subtasks include converting large text volumes into structured databases or repositories.
- Users often want to extract three kinds of information from documents:
- Named entities
- Relations between entities
- Events
Named Entity Recognition (NER)
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Also known as named entity identification, entity chunking, or entity extraction.
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Identifies and classifies named entities in unstructured text into predefined categories.
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Categories include:
- Generic categories (e.g., person names, organizations, locations, time expressions, quantities, monetary values).
- More specific categories for PERSON (e.g., politician, scientist, sportsperson, filmstar, musician).
- Domain-specific categories (e.g., medical codes, clinical procedures, biological proteins, diseases).
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Example: "I hear Berlin is wonderful in the winter." (Berlin, Place; winter, Time)
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The term "named entity" first emerged at the Sixth Message Understanding Conference (MUC-6) in 1995.
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The Entity Detection and Tracking (EDT) task from the Automatic Content Extraction (ACE) conference (2003) proposed classifying entity mentions into equivalence classes to indicate the same entity.
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Other scientific events such as CoNLL03, IREX, and TREC Entity Track have contributed to NER by providing datasets.
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A named entity is a word or phrase that uniquely identifies an item from a set that shares similar attributes (e.g., people, places, organizations).
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Example: "Cristiano Ronaldo dos Santos Aveiro GOIH COMM" (Person)
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NER tools support various labelling categories.
How is NER Used?
- Used in a variety of applications.
- Customer support: categorizing user requests, complaints, and questions by keywords to reduce response time.
- Healthcare: helping doctors quickly understand reports by extracting essential information.
- Search engines: optimizing search query analysis and result relevancy.
- Human resources: improving internal hiring processes by summarizing applicant CVs.
- Social media: analyzing user-generated content for insights.
Formal Definition of NER
- Given a sequence of tokens (S = <W₁, W2, ..., Wn>), NER outputs a list of tuples.
- Tuple form: <Is, Ie, t>
- Is: starting index of the named entity
- Ie: ending index of the named entity
- t: the pre-defined category of the named entity.
- Example: "Michael Jeffrey Jordan was born in Brooklyn, New York”
- <W₁ W₂ W₃, Person>
Flat vs Nested NER
- Flat NER only considers entities with non-overlapping spans.
- Nested NER handles nested entities where one entity can be a part of another.
- Nested NER is more generalized than flat NER.
NER Datasets
- Numerous tagged corpora (collections of annotated documents) are available for various entities and categories.
- Examples include MUC-6, MUC-6 Plus, OntoNotes, W-NUT, WikiGold, etc.
- Datasets cover diverse sources like Wall Street Journal, New York Times news, Wikipedia, news, and more.
NER Tools
- Off-the-shelf NER tools are provided by academia and industry to aid in projects
- Examples include: StanfordCoreNLP, OSU Twitter NLP, Illinois NLP, NeuroNER, and more.
NER Evaluation Metrics
- Common metrics include: precision, recall, and F-score.
- Precision: correctly recognized entities / total recognized entities.
- Recall: correctly recognized entities / total entities.
- F-score: harmonic mean of precision and recall.
- Exact match: entities are correctly recognized both in boundary and categories simultaneously.
- Relaxed match: boundary correctness is less important, and boundary errors are less damaging.
Traditional Approaches to NER
- Rule-based (knowledge-based): relies on predefined lexicons, hand-crafted rules, and domain knowledge.
- Unsupervised learning: utilizes unlabeled data to improve recall of NER systems.
- Feature-based supervised learning: leverages word-level features, gazetteers, and document features using machine learning algorithms.
NER Using Deep Learning
- Deep learning models have demonstrated state-of-the-art performance in NER.
- Input representations can use pre-trained word embeddings.
- Context encoders can use various architectures.
- Tag decoders use methods like MLPs and CRF.
Approaches to Nested NER
- Rule-based: leverages predefined rules, lexicons, and domain knowledge to identify entities.
- Layered-based; uses multiple layers (or levels) based on the hierarchical structure.
- Region-based: treat nested NER as a multiclass classification task.
- Hypergraph-based: leverage hypergraph structures to represent nested relationships of entities.
- Transition-based: parses sentences left-to-right to build entity structures.
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Description
Explore the fundamentals of Information Extraction (IE) and Named Entity Recognition (NER) in this quiz. Learn about the techniques used to convert unstructured text into structured data and the different categories for named entities. Test your understanding of these essential concepts in Natural Language Processing.
