75,116 research outputs found
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Query generation from multiple media examples
This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches.Comment: Accepted to SIGIR 201
Hybrid Search: Effectively Combining Keywords and Semantic Searches
This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontologybased search and keyword-based matching. Hybrid search smoothly copes with
lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce
plc for searching technical documentation about jet engines
Multi-Document Summarization via Discriminative Summary Reranking
Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models can always produce high-quality summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach
Data-driven Job Search Engine Using Skills and Company Attribute Filters
According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users.Comment: 8 pages, 10 figures, ICDM 201
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