Text Mining: Information Extraction Basics

Named Entity Recognition (NER) is a technique that identifies and classifies named entities (people, places, organizations, etc.) in text.

NER helps in categorizing user requests, complaints, and questions, improving response times and customer service.

NER extracts essential information from medical reports, simplifying the analysis of data and enhancing patient care.

NER analyzes search queries and text, improving the relevance and speed of search results.

NER assists in categorizing employee complaints and questions, optimizing internal workflows and speeding up processes, like resume analysis.

NER identifies relevant entities in vast volumes of social media data, offering valuable insights for social media analysis and NLP tasks.

Flat NER focuses on identifying simple entities with non-overlapping spans. Each token belongs to only one entity.

Nested NER recognizes complex entities with nested structures. A token can belong to multiple entities, forming hierarchies.

A named entity recognition (NER) task where entities can be nested within each other, allowing for a more complex and hierarchical representation of the data. Think of Russian nesting dolls where an entity contains another entity, which in turn could contain yet another one.

A simplified version of nested NER, where entities are treated as flat and independent of each other. Think of all entities on the same level, with no nesting.

This dataset focuses on various named entities like location, product, person, and more, and it's particularly interesting because it analyzes user-generated content like tweets and comments, providing insights into real-world language.

This dataset is a valuable resource for NER in the medical domain, containing mentions of various diseases and their classifications, with connections to medical databases like OMIM and MeSH.

This dataset is a comprehensive collection of entities, relations, and events in multiple languages. It's particularly noteworthy for its high percentage of nested entities, making it valuable for testing nested NER models.

A collection of documents that have been labeled with specific information, like named entities. This serves as training data for NER models.

These datasets, focusing on four entities (person, location, organization, and miscellaneous), were created from news articles in several languages. They are widely used for training and evaluating NER models.

This dataset annotates a huge corpus of text in multiple languages, focusing on entities but also including information about relationships between them. This dataset is valuable for research in various areas, including natural language processing and machine learning.

These approaches do not require labeled training data and rely on pre-defined lists of entities, hand-crafted rules, and specific domain knowledge to identify entities.

These approaches aim to learn patterns from unlabeled text to identify entities. They often use minimal labeled data.

In these systems, the model learns from labeled training data and uses features like word capitalization, context or part-of-speech tags to predict entity types.

A method used in supervised NER where a sequence of tags is assigned to each word in a sentence. Often used for identifying named entities.

It's a measure of how much information a term holds. It can be used in unsupervised NER to identify entities.

A method used in unsupervised NER for extracting entities. It can be used for identifying entities in short phrases or sentences.

A type of Named Entity Recognition (NER) evaluation that considers an entity correct if its type is correct regardless of its boundaries. It allows for partial overlaps between predicted and actual boundaries.

Entities that are correctly identified and categorized by a Named Entity Recognition (NER) system.

Entities not recognized by a Named Entity Recognition (NER) system, despite being present in the ground truth.

Entities recognized by a Named Entity Recognition (NER) system, but incorrectly classified or misidentified.

A Named Entity Recognition (NER) evaluation method that calculates the F-score separately for each entity type and then averages them.

A Named Entity Recognition (NER) evaluation method that sums up the individual false negatives, false positives, and true positives across all entity types before calculating precision, recall, and F-score.

A type of Named Entity Recognition (NER) evaluation that requires a system to correctly identify both the boundaries and the category type of an entity.

A Named Entity Recognition (NER) task that identifies and classifies named entities (people, places, organizations, etc.) in text.

This tagging scheme labels each token as 'B' (Beginning), 'I' (Inside), or 'O' (Outside) of a named entity. For example, 'John' would be tagged as 'B-PERSON', 'Smith' as 'I-PERSON', and 'is' as 'O'.

CRF-based NER systems utilize Conditional Random Fields, which are a type of probabilistic model that considers the relationship between neighboring tokens when predicting tags. This helps capture the context surrounding the entity.

These models learn hidden patterns and features directly from the data, eliminating the need for manual feature engineering. This makes them adaptable and efficient.

Word2Vec, GloVe, fastText, and SENNA. These methods generate numerical representations of words, capturing semantic relationships, which can be used as input for deep learning models.

Representations based on individual characters, useful for handling out-of-vocabulary words and capturing morpheme-level information.

Input representation, in the context of deep learning NER, refers to how the text is encoded and transformed into a suitable format for the model. This typically involves converting words into numerical representations (embeddings).

Combines word-level embeddings with character-level representations for richer context.

Input representation, in the context of deep learning NER, refers to how the text is encoded and transformed into a suitable format for the model. This typically involves converting words into numerical representations (embeddings).

Deep contextualized language models like BERT provide robust representations that capture the meaning of words in context.

In deep learning based NER, the 'embedding layer' converts words into vector representations, capturing their semantic meaning. These vectors are then fed into other layers of the neural network for processing.

Process input sequences to identify relationships within the text, using architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.

A multi-class classification problem where each word is assigned a tag based on its context. The model predicts tags independently for each word.

Models the conditional probability of a sequence of tags, considering dependencies between neighboring tags. It helps to capture the relationships between adjacent words and their tags.

Used to decode tags, often faster to train than CRFs when there are many entity types.

A toolkit for training word-level embeddings, often used for creating domain-specific representations.