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Query expansion is the process of reformulating a seed query to improve retrieval performance. This involves adding synonyms, derived words, and related terms. Important in NLP to handle the diversity of language and improve the chances of retrieving relevant documents.

This model ranks documents based on the probability that a given document will be relevant to a user's query. It is important in NLP because it allows incorporating uncertainty and partial matching, which is closer to how humans assess relevance.

In the context of information retrieval, precision is the fraction of retrieved documents that are relevant to the query. High precision means that an algorithm returned substantially more relevant results than irrelevant.

LSA is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. It's significant for capturing the hidden conceptual connections between words that may be missed with simple term matching.

The Vector Space Model (VSM) represents text documents as vectors in a multi-dimensional space, where each dimension corresponds to a term from the document corpus. It is important in NLP for calculating the relevance of documents to a query, based on the cosine similarity between their vector representations.

Ranking in information retrieval involves ordering documents by relevance to a query. This is essential in NLP applications like search engines, where the goal is to present the most relevant results first to the user.

An Information Retrieval System is software that provides access to a large collection of information and finds relevant pieces in response to a user's query. Key in NLP as it bridges user-centric queries and the vast documented knowledge.

Stop words are commonly used words (such as 'the', 'is', 'at') that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them. In NLP, removing stop words can help in focusing on the more meaningful words relevant to a given task.

The Boolean Model of information retrieval is a framework for interpreting queries as boolean expressions. While not typically nuanced, it's a foundational model that paves the way to more sophisticated techniques in NLP.

Document retrieval is the task of finding documents relevant to a user's query from a large collection. In NLP, this involves processing and understanding human language to match queries with correct documents.

Recall is the fraction of the relevant documents that have been retrieved over the total amount of relevant documents. It measures the ability of a system to present all relevant items and is important in scenarios where missing any relevant item is critical.

Relevance Feedback is a feature of information retrieval systems where the systems use the information about whether or how relevant the retrieved documents are to refine the search results. It's a crucial mechanism in NLP-driven search engines to improve result accuracy based on user interaction.

A Language Model for IR is a probabilistic model used to predict the distribution of words in a document. Utilizing approaches like uni-grams, bi-grams, etc., it's applied in NLP to estimate how likely a document is to be relevant to a query.

Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that reflects how important a word is to a document in a collection or corpus. It is often used in information retrieval and text mining as a weighting factor and is critical for scoring and ranking a document's relevance given a user query.