Comparative Web Search Questions

Bondarenko, Alexander; Braslavski, Pavel; Völske, Michael; Aly, Rami; Fröbe, Maik; Panchenko, Alexander; Biemann, Chris; Stein, Benno; Hagen, Matthias

doi:10.1145/3336191.3371848

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…This allows us to develop and maintain services such as the ChatNoir web search engine [Be18], the argument search engine args.me [Wa17], but also facilitates individual research projects which rely on utilizing the bundled computational power and storage capacity of the capable Hadoop, Kubernetes, and Ceph clusters. A few recent examples are the analysis of massive query logs [Bo20], a detailed inquiry into near-duplicates in web search [Fr20b,Fr20a], the preparation, parsing, and assembly of large NLP corpora [Be20,Ke20], and the exploration of large transformer models for argument retrieval [AP20].…”

Section: Discussionmentioning

confidence: 99%

Web Archive Analytics

Völske,

Bevendorff,

Kiesel

et al. 2021

Preprint

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Web archive analytics is the exploitation of publicly accessible web pages and their evolution for research purposes-to the extent organizationally possible for researchers. In order to better understand the complexity of this task, the first part of this paper puts the entirety of the world's captured, created, and replicated data (the "Global Datasphere") in relation to other important data sets such as the public internet and its web pages, or what is preserved thereof by the Internet Archive. Recently, the Webis research group, a network of university chairs to which the authors belong, concluded an agreement with the Internet Archive to download a substantial part of its web archive for research purposes. The second part of the paper in hand describes our infrastructure for processing this data treasure: We will eventually host around 8 PB of web archive data from the Internet Archive and Common Crawl, with the goal of supplementing existing large scale web corpora and forming a non-biased subset of the 30 PB web archive at the Internet Archive.

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Section: Discussionmentioning

confidence: 99%

Web Archive Analytics

Völske,

Bevendorff,

Kiesel

et al. 2021

Preprint

Self Cite

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“…The majority of sentences compare more than one pair of objects across multiple parameters (i.e., sentences often contain more than one aspect or predicate). As the NLU processed not statements but questions, for the further improvement of the dataset, we could use comparative questions from (Bondarenko et al, 2020a). This dataset is essentially similar to the ones by (Arora et al, 2017;Kessler and Kuhn, 2014).…”

Section: Natural Language Understandingmentioning

confidence: 99%

“…Comparison of objects of a particular class (e.g., holiday destinations, mobile phones, programming languages) is an essential daily task that many individuals require every day. According to Bondarenko et al (2020a), comparative questions constitute around 3% of queries submitted to major search engines-a non-negligible amount. Answering a comparative question (What is better, X or Y?)…”

Section: Introductionmentioning

confidence: 99%

Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

Chekalina¹,

Bondarenko²,

Biemann³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.

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“…A good fraction of web queries revolve around quantities of entities: looking up, filtering, comparing and aggregating quantitative properties such as heights of buildings, running times of athletes, goals or scoring rates of footballers, energy consumption of electric cars, etc. [4,7,16]. In this paper we focus on quantity filters [16,17], an important class of queries and also a building block for comparative search.…”

Section: Introductionmentioning

confidence: 99%

Extracting Contextualized Quantity Facts from Web Tables

Pal

Razniewski

et al. 2021

Proceedings of the Web Conference 2021

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Quantity queries, with filter conditions on quantitative measures of entities, are beyond the functionality of search engines and QA assistants. To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. This involves recognizing quantities, with normalized values and units, aligning them with the proper entities, and contextualizing these pairs with informative cues to match sophisticated queries with modifiers. Our method includes a new approach to aligning quantity columns to entity columns. Prior works assumed a single subject-column per table, whereas our approach is geared for complex tables and leverages external corpora as evidence. For contextualization, we identify informative cues from text and structural markup that surrounds a table. For querytime fact ranking, we devise a new scoring technique that exploits both context similarity and inter-fact consistency. Comparisons of our building blocks against state-of-the-art baselines and extrinsic experiments with two query benchmarks demonstrate the benefits of our method.

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Comparative Web Search Questions

Cited by 16 publications

References 8 publications

Web Archive Analytics

Web Archive Analytics

Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

Extracting Contextualized Quantity Facts from Web Tables

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