NLP Chapter: Extracting Information from Text

Introduction to Natural Language Processing - Extracting Information from Text

• The goal of the chapter is to explore methods for extracting structured data from unstructured text.
• This involves identifying entities and relationships within the text.
• Information extraction is a process of converting unstructured data into structured data.
• Structured data involves a predictable organization of entities and relationships.
• Examples of the type of relationships are between companies and locations.
• A system needs to be able to identify companies operating within a specific location.
• This process can use corpora for relevant information

Information Extraction

• Information comes in many forms, structured, and unstructured.
• Structured data is highly organized with entities and relationships.
• Examples of data relationships include those between companies and their locations.
• A system analyzing companies should determine where a company operates.
• The opposite is also important, given a location, find the companies that operate there

Information Extraction - Tables

• If data is in tabular form (like the example given), information extraction is straightforward.
• Extracting information can be simple, such as using Python tuples. (entity, relation, entity)
• A question like "Which of the organizations operate in Atlanta" can be translated into code.

Information Extraction - Example

• Information extraction becomes complex when dealing with unstructured text.
• An example of unstructured text is analyzed which includes details about a company moving agencies.
• This example text is unstructured and needs a different method of extracting the information compared to retrieving data from a table.

Information Extraction - Methods

• The conversion of unstructured, natural language text information into structured data is termed Information Extraction.
• It is important to convert unstructured data into structured data.
• The conversion of raw text into structured data is used in many fields including business intelligence, media analysis, and email scanning

Information Extraction Architecture

• A simple information extraction system involves segmenting text into sentences, tokenizing words, and tagging parts of speech.
• Named entity recognition helps to identify and label key entities.
• Identifying entities of a specific interest, like companies or locations, comes from this process.
• Relation recognition identifies the relationships between these entities.

Information Extraction Architecture - Implementation

• Libraries like NLTK can be used to perform these tasks (segmentation, tokenization, part of speech tagging).
• NLTK libraries can automatically connect these processes.
• These tasks involve tokenization, part of speech tagging, and named entity recognition to discover entities of interest from the text.

Chunking

• The method of chunking is a fundamental technique for named entity recognition.
• Chunking segments and labels multi-token sequences.
• Example illustrated includes word-level tokenization and part-of-speech tagging, and then chunking.
• The output should include no overlap in the source text.

Noun Phrase Chunking

• Chunking, particularly NP-chunking, isolates noun phrases within text.
• These units are crucial for understanding relationships.
• An example shows how wall street journal text is chunked into proper noun phrases.
• NP-chunking is used to extract named entities within the text

Noun Phrase Chunking - POS tags

• Part-of-speech (POS) tags are helpful in identifying noun phrases.
• By using POS tags, we can identify patterns for chunking.
• Each chunk should contain patterns of POS tags that indicate a noun phrase.
• Parsing the text, based on chunk grammar definitions should show noun phrases

Chunking with Regular Expressions

• Defining patterns using regular expressions helps in constructing grammars for chunking.
• Combining regular expressions with tokenization leads to a more precise chunking method.
• The use of regular expressions offers more opportunities to adjust the method, allowing the user to fine-tune the method.
• It is possible to create multiple rules to define different chunks

Chunking with Regular Expressions - Example

• A simple grammar can include rules for determiners, adjectives, and nouns.
• Proper nouns can also be handled as separate rules in the grammar.
• This technique uses a text example of regular expressions and grammar.

Chunking with Regular Expressions - Matching

• Leftmost matching takes precedence when multiple patterns overlap.
• Extracting consecutive nouns is an example of a task that involves checking overlapping words or patterns.
• If consecutive nouns have to be extracted, a chunk can only include two consecutive nouns and not three.

Exploring Text Corpora

• Interrogating tagged corpora can identify phrases matching sequences of part-of-speech tags.
• Chunking simplifies this process, making it more efficient.
• The conversion of tagged words to chunks can be faster with more efficient chunking processes

Chinking

• Excluding certain tokens for a specific task is sometimes useful.
• Chinking is the technique for removing sequences of tokens that are not part of a chunk.
• Three methods of chinking are possible when multiple conditions are applied.
• These apply to chunks, the central part, and the edges

Chinking - Example

• Removing tokens like "barked" and "at" from a sentence using specific rules.
• This is demonstrated using specific examples from a sentence.

Representing Chunks - Tags Versus Trees

• Chunks can be represented as tags or trees.
• A common representation called IOB is a method for tagging chunks.
• The IOB method labels tokens with whether they are inside, outside, or a beginning of a chunk.
• A specific chunk type, like NP (noun phrase), is then included in the labeling

Representing Chunks - Tags Versus Trees - Example

• The IOB method example includes a structured representation with POS (part of speech) and chunk tags, demonstrating the system.

Reading IOB Format and the CONLL-2000 Chunking Corpus

• The CONLL-2000 corpus is a frequently used dataset.
• NLTK allows access to this corpus for processing chunks that is pre-tagged with chunk tags and POS tags.
• It is possible to access chunks from this corpus, and the corpus is frequently used to develop and test named entity recognition systems

Reading IOB Format and the CONLL-2000 Chunking Corpus - Example

• Shows an example for extracting the 100th sentence in the "train" segment from the corpus

Trees

• Trees are structures showing relationships between labeled nodes stemming from a root.
• Trees follow the structure of a family tree and show who is the parent and who is the child.
• Node labels and children are used to create a tree representation.
• Complex tree structures can be represented using trees.

Trees -Methods

• Using tree methods effectively reduces ambiguity and enhances understanding.
• Tree representations are useful, and have advantages during testing and development, including providing a more visually interpretable format.

Named Entity Recognition

• Named entity recognition (NER) identifies significant entities in text.
• Specific types of entities are recognized, such as organizations, people, locations, dates, and money amounts.
• Examples of entities are shown.

Named Entity Recognition - Identifying

• Recognizing entities from a list, looking up terms in dictionaries or lists of words or names.
• NER subtasks include identifying the boundaries and type of named entities.

Named Entity Recognition - Limitations

• The task of recognizing named entities is challenging.
• Ambiguity in words and frequent changes in named entities for people, organizations, and locations are significant challenges.
• The need for flexible and adaptable methods is evident to tackle the challenges.

Named Entity Recognition - QA Systems

• QA, question answering, systems improve information retrieval.
• These systems aim to extract only important parts of text and remove unnecessary bits of information, avoiding confusion.
• Examples of this are shown and their applications are described.

Relation Extraction

• Relation extraction identifies relationships between recognized entities.
• It is important to find methods to retrieve relations from natural language text.
• Relationships can be represented as triples (X, α, Y).

Relation Extraction - Using Regular Expressions

• Specific patterns within the text are used to find relations.
• This is demonstrated by the use of a special regular expression in the example.

Exercises

• Practical exercises are included to practice and demonstrate what has been learned from the lecture.
• The exercises help to solidify the learning by implementing practical examples.

This chapter delves into methods for extracting structured information from unstructured text. Learn about identifying entities and their relationships, as well as converting raw data into a format that is easy to analyze. Understand how systems utilize corpora to gather relevant data on companies and locations.