Using search session context for named entity recognition in query

Du, Junwu; Zhang, Zhimin; Yan, Jun; Cui, Yan; Chen, Zheng

doi:10.1145/1835449.1835605

Cited by 23 publications

(12 citation statements)

References 2 publications

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“…Instead of just using the current user query, Du et al [8] used the full search session to identify and classify named entities. Two search session features were used, namely, 'class features', the class of the named entity appearing in the previous query in the session, and the 'overlap feature' of the words between the previous and the current query.…”

Section: Related Workmentioning

confidence: 99%

Mining named entities from search engine query logs

Alasiry

Levene

Poulovassilis

2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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We present a seed expansion based approach to classify named entities in web search queries. Previous approaches to this classification problem relied on contextual clues in the form of keywords surrounding a named entity in the query. Here we propose an alternative approach in the form of a Bag-ofContext-Words (BoCW) that is used to represent the context words as they appear in the snippets of the top search results for the query. This is particularly useful in the case where the query consists of only the named entity without any context words, since in the previous approaches no context is discovered. In order to construct the BoCW, we employ a novel algorithm, which iteratively expands a Class Vector that is created through expansion by gradually aggregating the BoCWs of similar named entities appearing in other queries. We provide comprehensive experimental evidence using a commercial query log showing that our approach is competitive with existing approaches.

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Section: Related Workmentioning

confidence: 99%

Mining named entities from search engine query logs

Alasiry

Levene

Poulovassilis

2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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“…Query annotation is a common practice in query understanding, such as query segmentation [11,25], name entity recognition [9,10] and part-of-speech tagging [2]. Query segmentation techniques segment a query into a number of semantic units, which is a basic preprocess in information retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…Users commonly keep a substring invariable when reformulating queries to clarify their intents, moreover, this invariable substring generally corresponds to a semantic unit [9]. Instead of pre-segmenting the individual queries into words, we employ a naï ve strategy.…”

Section: Learning the Model Lexiconmentioning

confidence: 99%

Role-explicit query identification and intent role annotation

Ren

2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Understanding the information need or intent encoded within a query has long been regarded as an essential factor of effective information retrieval. For better query representation and understanding, two intent roles (kernel-object and modifier) are introduced to structurally parse a class of role-explicit queries, which constitute a majority of common user queries. Furthermore, we focus on two research problems: RP-1: Given a role-explicit query, how to identify the kernel-object and modifier, namely intent role annotation; RP-2: How to determine whether an arbitrary query is role-explicit or not. To solve RP-1, we propose a simplified word n-gram role model (SWNR), which quantifies the generating probability of a role-explicit query and performs intent role annotation effectively. Using a set of discriminative features, we build classifiers to address RP-2 in a supervised manner. The experimental results show that: (1) SWNR can achieve a satisfactory performance, more than 73% in terms of different metrics; (2) The classifiers can achieve more than 90% precision in identifying role-explicit queries; (3) Compared with traditional techniques for query representation and understanding, e.g., name entity recognition in query and class-level query intent inference, intent role annotation provides a more flexible framework and a number of applications can benefit from annotating role-explicit queries, such as intent mining and diversified document ranking.

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“…This is because queries are usually very short and, therefore, contextual information (i.e. words surrounding a word), which usually helps the disambiguation process in texts, is very limited see Du et al (2010) for an approach that uses queries in the same search session as a context to improve NER in a query). Moreover, very often, terms in queries are not in standard form (e.g.…”

Section: Related Workmentioning

confidence: 99%

Entity Type Disambiguation in User Queries

Bazzanella

Stoermer

Bouquet

2011

J. Info. Know. Mgmt.

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Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-speci¯c information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a speci¯c entity. We believe that an important¯rst step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper, we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user speci¯es in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The bene¯cial impact of this approach for the development of new search systems is discussed.

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Using search session context for named entity recognition in query

Cited by 23 publications

References 2 publications

Mining named entities from search engine query logs

Mining named entities from search engine query logs

Role-explicit query identification and intent role annotation

Entity Type Disambiguation in User Queries

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