Named Entity Recognition and Classification is the task of extracting from text, instances of different entity classes such as person, location, or company. This task has recently been applied to web search queries in order to better understand their semantics, where a search query consists of linguistic units that users submit to a search engine to convey their search need. Discovering and analysing the linguistic units comprising a search query enables search engines to reveal and meet users' search intents. As a result, recent research has concentrated on analysing the constituent units comprising search queries. However, since search queries are short, unstructured, and ambiguous, an approach to detect and classify named entities is presented in this thesis, in which queries are augmented with the text snippets of search results for search queries. The thesis makes the following contributions: 1. A novel method for detecting candidate named entities in search queries, which utilises both query grammatical annotation and query segmentation. 2. A novel method to classify the detected candidate entities into a set of target entity classes, by using a seed expansion approach; the method presented exploits the representation of the sets of contextual clues surrounding the entities in the snippets as vectors in a common vector space. 3. An exploratory analysis of three main categories of search refiners: nouns, verbs, and adjectives, that users often incorporate in entity-centric queries in order to further refine the entity-related search results. 4. A taxonomy of named entities derived from a search engine query log. By using a large commercial query log, experimental evidence is provided that the work presented herein is competitive with the existing research in the field of entity recognition and classification in search queries.