s-grams: Defining generalized n-grams for information retrieval

Järvelin, Anni; Järvelin, Antti; Järvelin, Kalervo

doi:10.1016/j.ipm.2006.09.016

Cited by 36 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sometimes, the terms may be very similar, but not identical, due to misspelling or different prefixes/suffixes. To capture content similarity even in those cases, we adopt a Jaccard index on character tri-grams [Järvelin et al 2007]. Let T (q) be the character tri-grams from the terms of query q, we define the similarity σ jaccard as follows.…”

Section: Unsupervised Learning Approachmentioning

confidence: 99%

Discovering tasks from search engine query logs

Lucchese

Orlando

Perego

et al. 2013

ACM Trans. Inf. Syst.

102

View full text Add to dashboard Cite

Although Web search engines still answer user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g., finding a recipe, booking a flight, reading online news, etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a user search session), which refer to the same need. Second, we discover collective tasks by aggregating similar user tasks, possibly performed by distinct users. To discover user tasks, we propose query similarity functions based on unsupervised and supervised learning approaches. We present a set of query clustering methods that exploit these functions in order to detect user tasks. All the proposed solutions were evaluated on a manually-built ground truth, and two of them performed better than state-of-the-art approaches. To detect collective tasks, we propose four methods that cluster previously discovered user tasks, which in turn are represented by the bag-of-words extracted from their composing queries. These solutions were also evaluated on another manually-built ground truth.

show abstract

Section: Unsupervised Learning Approachmentioning

confidence: 99%

Discovering tasks from search engine query logs

Lucchese

Orlando

Perego

et al. 2013

ACM Trans. Inf. Syst.

102

View full text Add to dashboard Cite

show abstract

“…Sometime, such terms may be very similar, but not identical, due to mispelling, or di↵er-ent prefixes/su xes. To capture content distance between queries, we adopt a Jaccard index on tri-grams [10]. Let T (q) be the tri-grams resulting from the terms of query q, we define the distance µ jaccard as follows:…”

Section: Feature Selectionmentioning

confidence: 99%

Identifying task-based sessions in search engine query logs

Lucchese

Orlando

Perego

et al. 2011

Proceedings of the Fourth ACM International Conference on Web Search and Data Mining

101

134

View full text Add to dashboard Cite

The research challenge addressed in this paper is to devise e↵ective techniques for identifying task-based sessions, i.e. sets of possibly non contiguous queries issued by the user of a Web Search Engine for carrying out a given task. In order to evaluate and compare di↵erent approaches, we built, by means of a manual labeling process, a ground-truth where the queries of a given query log have been grouped in tasks. Our analysis of this ground-truth shows that users tend to perform more than one task at the same time, since about 75% of the submitted queries involve a multi-tasking activity. We formally define the Task-based Session Discovery Problem (TSDP) as the problem of best approximating the manually annotated tasks, and we propose several variants of well known clustering algorithms, as well as a novel e cient heuristic algorithm, specifically tuned for solving the TSDP. These algorithms also exploit the collaborative knowledge collected by Wiktionary and Wikipedia for detecting query pairs that are not similar from a lexical content point of view, but actually semantically related. The proposed algorithms have been evaluated on the above groundtruth, and are shown to perform better than state-of-the-art approaches, because they e↵ectively take into account the multi-tasking behavior of users.

show abstract

“…They can also be traced down to their word-stem. This stemming makes it possible to combine sentences in different tenses without having them in different grammatical forms [5]. Another step that usually needs to be taken is that stop and small words need to be deleted since they do not contain relevant information of the text and do not need to be analyzed by the machine.…”

Section: Discussion Of Methodsmentioning

confidence: 99%