Named entity recognition in query

By Jiafeng Guo, Gu Xu, Xueqi Cheng, Hang Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009

435 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ec4ea97c9b04ae044d8be71fd65b13b2edee1b03

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In Management Science and Operations Research

Citations in the corpus

BTM: Topic Modeling over Short Texts (2014, background): “BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information.”
Few-Shot Named Entity Recognition: A Comprehensive Study (2020, background): “NER serves as an important ﬁrst component for tasks such as information extraction (Ritter et al., 2012), information retrieval (Guo et al., 2009), question answering (Mollá et al., 2006), task-oriented dialogues (Peng et al., 2020a; Gao et al., 2019) and other language understanding applications…”
A biterm topic model for short texts (2013, background): “Uncovering the topics of such short texts is crucial for a wide range of content analysis tasks, such as content characterizing [26, 35, 14], user interest proﬁling [32], emerging topic detecting [20] and so on.”
Semantic Matching in Search (2014, background): “Named entity recognition in query [78, 190], in parallel, and named entity recognition in document should be helpful as well.”
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “Some researchers have applied this model to LDA training, such as [13, 16, 52, 79, 203].”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, background): “…et al., 2012; Yahya et al., 2012; Berant et al., 2013; Kwiatkowski et al., 2013), the Web search and information retrieval (IR) community has focused on telegraphic queries (Guo et al., 2009; Sarkas et al., 2010; Li et al., 2011; Pantel et al., 2012; Lin et al., 2012; Sawant and Chakrabarti, 2013).”
A Survey on Deep Learning for Named Entity Recognition (2018, background): “NER not only acts as a standalone tool for information extraction (IE), but also plays an essential role in a variety of natural language processing (NLP) applications such as information retrieval [2], [3], automatic text summarization [4], question answering [5], machine translation [6], and knowl”
Learning joint query interpretation and response ranking (2012, background): “[15] proposed a probabilistic language model through weak supervision that learns to associate, e.”
Entity Disambiguation with Web Links (2015, methodology): “An entity prior is used in generative models (Guo et al., 2009; Han and Sun, 2011) and in supervised systems that incorporate diverse features (Radford et al.”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Named entities usually appear in search queries and they are undoubtedly ambiguous [51].”
Piggyback: Using Search Engines for Robust Cross-Domain Named Entity Recognition (2011, methodology): “Guo et al. (2009) exploit query logs for NER in queries.”
Document Retrieval Model Through Semantic Linking (2017, background): “There is a progressive body of work in the literature that focus on query analysis and segmentation [11, 24].”
Discovering and understanding word level user intent in Web search queries (2015, background): “2009 [40]) do have named entities or multiword expressions (roger federer, summa cum laude) within them.”
LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement (2026, unknown): “This paper is motivated by a key observation from real-world document retrieval scenarios: user queries are typically keyword-anchored [22, 44, 52].”