Using Metafeatures to Increase the Effectiveness of Latent Semantic Models in Web Search

Abstract

Using metafeatures to increase the effectiveness of latent semantic models in web search Borisov, A.; Serdyukov, P.; de Rijke, M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTIn web search, latent semantic models have been proposed to bridge the lexical gap between queries and documents that is due to the fact that searchers and content creators often use different vocabularies and language styles to express the same concept. Modern search engines simply use the outputs of latent semantic models as features for a so-called global ranker. We argue that this is not optimal, because a single value output by a latent semantic model may be insufficient to describe all aspects of the model's prediction, and thus some information captured by the model is not used effectively by the search engine.To increase the effectiveness of latent semantic models in web search, we propose to create metafeatures-feature vectors that describe the structure of the model's prediction for a given querydocument pair-and pass them to the global ranker along with the models' scores. We provide simple guidelines to represent the latent semantic model's prediction with more than a single number, and illustrate these guidelines using several latent semantic models.We test the impact of the proposed metafeatures on a web document ranking task using four latent semantic models. Our experiments show that (1) through the use of metafeatures, the performance of each individual latent semantic model can be improved by 10.2% and 4.2% in NDCG scores at truncation levels 1 and 10; and (2) through the use of metafeatures, the performance of a combination of latent semantic models can be improved by 7.6% and 3.8% in NDCG scores at truncation levels 1 and 10, respectively.

Weakly-supervised Contextualization of Knowledge Graph Facts

“…Note that we calculate these features for both facts f q and f c . The first of these feature templates measures normalized predicate frequency of each predicate p that participates in fact f (we also include the minimum, maximum and average value for each fact as metafeatures [9]). This is defined as the ratio of the size of the set of triples that have predicate p in the KG to the total number of triples:…”

Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

“…In the last decade, a wide spectrum of topic models has been proposed in academia and demonstrates promising performance. However, for industrial topic modeling, Probabilistic Latent Semantic Analysis (PLSA) [11] and Latent Dirichlet Allocation (LDA) [1] are the working horses so far [2] [31]. With the richness of the other topic models, to name a few, TOT [35], Bilingual Topic Model [7], Pair Model [14], GeoFolk [32], LATM [34] and Multifaceted Topic Model [33], we rarely witness employment of them in industrial applications.…”

Analyzing Two Automatic Latent Semantic Analysis (LSA) Assessment Methods (Inbuilt Rubric vs. Golden Summary) in Summaries Extracted from Expository Texts

“…semantic memory (e.g., Günther, Dudschig, & Kaup, 2015). Some of these applications are: identifying current tendencies in research (Aryal, Gallivan, & Tao, 2015;Wendy, How, & Atoum, 2014;Xu et al, 2015), improving search engines (Borisov, Serdyukov, & de Rijke, 2016;Ryan, Kaltman, Mateas, & Wardrip-Fruin, 2015), and producing keywords (Pu, Jin, Wu, Han, & Xue, 2015). LSA has also been applied in clinical domains, as automatically diagnosing psychological disorders (Cohen, Blatter, & Patel, 2008;Jorge-Botana, Olmos, & León, 2009) or improving tests used to prevent future risk of neuropsychological illness such as dementia (Pakhomov & Hemmy, 2014).…”