169 research outputs found

    Improving Neural Ranking Models with Traditional IR Methods

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    Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to create, and require a great deal of labeled data for specialized corpora. In this paper, we explore a low resource alternative which is a bag-of-embedding model for document retrieval and find that it is competitive with large transformer models fine tuned on information retrieval tasks. Our results show that a simple combination of TF-IDF, a traditional keyword matching method, with a shallow embedding model provides a low cost path to compete well with the performance of complex neural ranking models on 3 datasets. Furthermore, adding TF-IDF measures improves the performance of large-scale fine tuned models on these tasks.Comment: Short paper, 4 page

    Known by the Company it Keeps: Proximity-Based Indexing for Physical Content in Archival Repositories

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    Despite the plethora of born-digital content, vast troves of important content remain accessible only on physical media such as paper or microfilm. The traditional approach to indexing undigitized content is using manually created metadata that describes content at some level of aggregation (e.g., folder, box, or collection). Searchers led in this way to some subset of the content often must then manually examine substantial quantities of physical media to find what they are looking for. This paper proposes a complementary approach, in which selective digitization of a small portion of the content is used as a basis for proximity-based indexing as a way of bringing the user closer to the specific content for which they are looking. Experiments with 35 boxes of partially digitized US State Department records indicate that box-level indexes built in this way can provide a useful basis for search

    Leveraging Formulae and Text for Improved Math Retrieval

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    Large collections containing millions of math formulas are available online. Retrieving math expressions from these collections is challenging. Users can use formula, formula+text, or math questions to express their math information needs. The structural complexity of formulas requires specialized processing. Despite the existence of math search systems and online community question-answering websites for math, little is known about mathematical information needs. This research first explores the characteristics of math searches using a general search engine. The findings show how math searches are different from general searches. Then, test collections for math-aware search are introduced. The ARQMath test collections have two main tasks: 1) finding answers for math questions and 2) contextual formula search. In each test collection (ARQMath-1 to -3) the same collection is used, Math Stack Exchange posts from 2010 to 2018, introducing different topics for each task. Compared to the previous test collections, ARQMath has a much larger number of diverse topics, and improved evaluation protocol. Another key role of this research is to leverage text and math information for improved math information retrieval. Three formula search models that only use the formula, with no context are introduced. The first model is an n-gram embedding model using both symbol layout tree and operator tree representations. The second model uses tree-edit distance to re-rank the results from the first model. Finally, a learning-to-rank model that leverages full-tree, sub-tree, and vector similarity scores is introduced. To use context, Math Abstract Meaning Representation (MathAMR) is introduced, which generalizes AMR trees to include math formula operations and arguments. This MathAMR is then used for contextualized formula search using a fine-tuned Sentence-BERT model. The experiments show tree-edit distance ranking achieves the current state-of-the-art results on contextual formula search task, and the MathAMR model can be beneficial for re-ranking. This research also addresses the answer retrieval task, introducing a two-step retrieval model in which similar questions are first found and then answers previously given to those similar questions are ranked. The proposed model, fine-tunes two Sentence-BERT models, one for finding similar questions and another one for ranking the answers. For Sentence-BERT model, raw text as well as MathAMR are used

    Mining Web Dynamics for Search

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    Billions of web users collectively contribute to a dynamic web that preserves how information sources and descriptions change over time. This dynamic process sheds light on the quality of web content, and even indicates the temporal properties of information needs expressed via queries. However, existing commercial search engines typically utilize one crawl of web content (the latest) without considering the complementary information concealed in web dynamics. As a result, the generated rankings may be biased due to the efficiency of knowledge on page or hyperlink evolution, and the time-sensitive facet within search quality, e.g., freshness, has to be neglected. While previous research efforts have been focused on exploring the temporal dimension in retrieval process, few of them showed consistent improvements on large-scale real-world archival web corpus with a broad time span.We investigate how to utilize the changes of web pages and hyperlinks to improve search quality, in terms of freshness and relevance of search results. Three applications that I have focused on are: (1) document representation, in which the anchortext (short descriptive text associated with hyperlinks) importance is estimated by considering its historical status; (2) web authority estimation, in which web freshness is quantified and utilized for controlling the authority propagation; and (3) learning to rank, in which freshness and relevance are optimized simultaneously in an adaptive way depending on query type. The contributions of this thesis are: (1) incorporate web dynamics information into critical components within search infrastructure in a principled way; and (2) empirically verify the proposed methods by conducting experiments based on (or depending on) a large-scale real-world archival web corpus, and demonstrated their superiority over existing state-of-the-art
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