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The term that describes "a word that is removed from the term list" is "stop word."

Stop words are common words in a language (such as "the," "and," "is," "in," etc.) that are often removed from text data when performing natural language processing tasks like text analysis or search engines. These words are removed because they don't carry significant meaning on their own and can be found in almost all documents. By removing stop words, the focus can be placed on more meaningful and important words, which helps in reducing noise and improving the efficiency of various NLP processes.

The other terms mentioned in your question have different meanings:

1. Tokenize: Tokenization is the process of breaking a text into individual units, such as words or phrases (tokens). It's a step often performed before analysis, where words or sentences are segmented into discrete components.

2. Phrase: A phrase typically refers to a group of words that convey a specific meaning but are smaller than a complete sentence. Phrases can be analyzed for their significance in text processing.

3. Corpus: A corpus is a collection of text documents or other textual data used for linguistic analysis or research. It's the dataset or body of text that researchers and NLP practitioners work with to develop and test algorithms, models, or linguistic theories.

Stopwords, or stoplist, are typically dropped from indexes within IR systems and not included in various text analyses as they are considered to be uninformative or meaningless. [1]

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[1] Michael W. Berry and Jacob Kogan, Text Mining: Applications and Theory, 2010.