Entity linking and retrieval for semantic search

Meij, Edgar; Balog, Krisztian; Odijk, Daan

doi:10.1145/2556195.2556201

Cited by 41 publications

(24 citation statements)

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“…Request permissions from Permissions@acm.org. text [15,23]. Concerning web search, annotating (open domain) queries with entities is an important part of query intent understanding [2,13,24].…”

Section: Introductionmentioning

confidence: 99%

The SMAPH system for query entity recognition and disambiguation

Cornolti

Ferragina

Ciaramita

et al. 2014

Proceedings of the First International Workshop on Entity Recognition &Amp; Disambiguation - ERD '14

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The SMAPH system implements a pipeline of four main steps: (1) Fetching -it fetches the search results returned by a search engine given the query to be annotated; (2) Spotting -search result snippets are parsed to identify candidate mentions for the entities to be annotated. This is done in a novel way by detecting the keywords-in-context by looking at the bold parts of the search snippets; (3) Candidate generation -candidate entities are generated in two ways: from the Wikipedia pages occurring in the search results, and from an existing annotator, using the mentions identified in the spotting step as input; (4) Pruning -a binary SVM classifier is used to decide which entities to keep/discard in order to generate the final annotation set for the query. The SMAPH system ranked third on the development set and first on the final blind test of the 2014 ERD Challenge short text track.

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Section: Introductionmentioning

confidence: 99%

The SMAPH system for query entity recognition and disambiguation

Cornolti

Ferragina

Ciaramita

et al. 2014

Proceedings of the First International Workshop on Entity Recognition &Amp; Disambiguation - ERD '14

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“…We could use POS of words as a feature for the model training of named entity recognition. An softwarespecific entity recognition and linking system would allow researchers to understand in depth the correlations between various software entities, thereby enabling more advanced software knowledge extraction and retrieval (semantic search [18]) centered around software entities.…”

Section: Towards Advanced Software-specific Information Extractionmentioning

confidence: 99%

Software-specific part-of-speech tagging

Xing

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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Part-of-speech (POS) tagging performance degrades on outof-domain data due to the lack of domain knowledge. Software engineering knowledge, embodied in textual documentations, bug reports and online forum discussions, is expressed in natural language, but is full of domain terms, software entities and software-specific informal languages. Such software texts call for software-specific POS tagging. In the software engineering community, there have been several attempts leveraging POS tagging technique to help solve software engineering tasks. However, little work is done for POS tagging on software natural language texts.In this paper, we build a software-specific POS tagger, called S-POS, for processing the textual discussions on Stack Overflow. We target at Stack Overflow because it has become an important developer-generated knowledge repository for software engineering. We define a POS tagset that is suitable for describing software engineering knowledge, select corpus, develop a custom tokenizer, annotate data, design features for supervised model training, and demonstrate that the tagging accuracy of S-POS outperforms that of the Stanford POS Tagger when tagging software texts. Our work presents a feasible roadmap to build software-specific POS tagger for the socio-professional contents on Stack Overflow, and reveals challenges and opportunities for advanced software-specific information extraction.

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“…There exists a large body of research on entity linking, retrieval and semantic search [4,3]. Among them, Ganesan and Zhai's Opinion Expansion (OE) and Query Aspect Modeling (QAM) [2] approaches are most relevant to ours.…”

Section: Related Workmentioning

confidence: 99%

Smith Search

Choi¹,

Kim²,

Choi³

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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Search engines have become an important decision-making tool today. Unfortunately, they still need to improve in answering complex queries. The answers to complex decisionmaking queries such as "best burgers and fries" and "good restaurants for anniversary dinner," are often subjective.The most relevant answer to the query can be obtained by only collecting people's opinions about the query, which are expressed in various venues on the Web. Collected opinions are converted into a "consensus" list. All of this should be processed at query time, which is impossible under the current search paradigm. To address this problem, we introduce Smith, a novel opinion-based restaurant search engine. Smith actively processes opinions on the Web, blogs, review boards, and other forms of social media at index time, and produces consensus answers from opinions at query time. The Smith search app (iOS) is available for download at

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Entity linking and retrieval for semantic search

Cited by 41 publications

References 0 publications

The SMAPH system for query entity recognition and disambiguation

The SMAPH system for query entity recognition and disambiguation

Software-specific part-of-speech tagging

Smith Search

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