Named entity recognition and classification in search queries

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

The Routledge Companion to Actor-Network Theory

This companion explores ANT as an intellectual practice, tracking its movements and engagements with a wide range of other academic and activist projects. Showcasing the work of a diverse set of ‘second generation’ ANT scholars from around the world, it highlights the exciting depth and breadth of contemporary ANT and its future possibilities. The companion has 38 chapters, each answering a key question about ANT and its capacities. Early chapters explore ANT as an intellectual practice and highlight ANT’s dialogues with other fields and key theorists. Others open critical, provocative discussions of its limitations. Later sections explore how ANT has been developed in a range of social scientific fields and how it has been used to explore a wide range of scales and sites. Chapters in the final section discuss ANT’s involvement in ‘real world’ endeavours such as disability and environmental activism, and even running a Chilean hospital. Each chapter contains an overview of relevant work and introduces original examples and ideas from the authors’ recent research. The chapters orient readers in rich, complex fields and can be read in any order or combination. Throughout the volume, authors mobilise ANT to explore and account for a range of exciting case studies: from wheelchair activism to parliamentary decision-making; from racial profiling to energy consumption monitoring; from queer sex to Korean cities. A comprehensive introduction by the editors explores the significance of ANT more broadly and provides an overview of the volume. The Routledge Companion to Actor-Network Theory will be an inspiring and lively companion to academics and advanced undergraduates and postgraduates from across many disciplines across the social sciences, including Sociology, Geography, Politics and Urban Studies, Environmental Studies and STS, and anyone wishing to engage with ANT, to understand what it has already been used to do and to imagine what it might do in the future