Metaphor Identification & Natural Language

Notes:

Natural language processing (NLP) is a field of computer science and artificial intelligence that focuses on the interaction between computers and human (natural) languages. NLP techniques can be used in the identification of metaphors, which are figures of speech that describe something by saying that it is something else, using the words “like” or “as.”

There are several ways in which NLP can be used in metaphor identification:

Lexical analysis: NLP techniques can be used to analyze the words and phrases used in a text to identify words that are being used metaphorically. For example, if a text uses the phrase “the stock market is a roller coaster,” the word “roller coaster” is being used metaphorically to describe the ups and downs of the stock market.
Semantic analysis: NLP techniques can be used to analyze the meaning of words and phrases in a text to identify metaphors. For example, if a text uses the phrase “the company is a ship sailing through rough waters,” the metaphor of a ship sailing through rough waters is used to describe the challenges faced by the company.
Syntactic analysis: NLP techniques can be used to analyze the syntax (grammar) of a text to identify the structure of a metaphor. For example, if a text uses the phrase “the project is a marathon,” the word “marathon” is being used metaphorically to describe the long and difficult nature of the project.

MIPVU (Metaphor Identification Procedure Vrije Universiteit) is a methodology for identifying metaphors in natural language texts. MIPVU is based on the conceptual metaphor theory, which proposes that metaphors are based on conceptual mappings between two domains of experience, such as love and war or time and money.

MIPVU involves several steps:

Preprocessing: In this step, the text is cleaned and prepped for analysis. This may involve removing punctuation, stemming or lemmatizing words, and other tasks.
Linguistic analysis: In this step, the text is analyzed linguistically to identify words and phrases that are being used metaphorically. This may involve identifying lexical and syntactic patterns that are characteristic of metaphors, such as the use of “as” or “like.”
Conceptual analysis: In this step, the identified metaphors are analyzed to identify the underlying conceptual mappings that they are based on. This may involve identifying the source and target domains of the metaphor and the nature of the mapping between them.

MIPVU is often used in research on metaphor and language processing, and has been applied to a variety of languages and domains. It can be a useful tool for identifying and analyzing metaphors in texts, and for understanding the role of metaphor in language and cognition.

Neural metaphor identification is the use of neural network models to identify and analyze metaphors in natural language texts. These models are trained to recognize patterns in language that are characteristic of metaphors, such as the use of words like “as” or “like” to indicate a metaphorical relationship.

There are a few different approaches to neural metaphor identification, depending on the specific goals and requirements of the task. Some common approaches include:

Sequence labeling: In this approach, the neural network is trained to label each word in a text with a label indicating whether it is part of a metaphor or not. This can be useful for identifying metaphors at the word or phrase level.
Sentence classification: In this approach, the neural network is trained to classify each sentence in a text as either metaphorical or non-metaphorical. This can be useful for identifying metaphors at the sentence level and for identifying the overall metaphorical content of a text.
Metaphor extraction: In this approach, the neural network is trained to identify and extract specific metaphors from a text. This can be useful for extracting and analyzing the underlying conceptual mappings that make up a metaphor.

Overall, neural metaphor identification involves training a neural network model on a large dataset of texts with labeled metaphors, and using the model to classify or extract metaphors from new texts. These models can be used to automatically identify and analyze metaphors in texts, and can be useful for a variety of applications, such as text analysis, natural language processing, and language learning.

Wikipedia:

Metaphor identification procedure (MIP)

References:

A method for linguistic metaphor identification: From MIP to MIPVU (2010)
Metaphor: A practical introduction (2010)