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Elements Of Episodic Memory

Due to the popularity of social media websites, extensive research efforts have been dedicated to tag-based social image search. Both visual information and tags have been investigated in the research field. However, most existing methods use tags and visual characteristics either separately or sequentially in order to estimate the relevance of images. In this paper, we propose an approach that simultaneously utilizes both visual and textual information to estimate the relevance of user tagged images. The relevance estimation is determined with a hypergraph learning approach. In this method, a social image hypergraph is constructed, where vertices represent images and hyperedges represent visual or textual terms. Learning is achieved with use of a set of pseudo-positive images, where the weights of hyperedges are updated throughout the learning process. In this way, the impact of different tags and visual words can be automatically modulated. Comparative results of the experiments conducted on a dataset including 370+images are presented, which demonstrate the effectiveness of the proposed approach.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2510&context=sis_research

Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search

大規模要約資源としてのNew York Times Annotated Corpus

The Art of Memory, was said to have been invented by a poet named Simonides (according to Cicero). In a bit of ancient forensics, Simonides had been able to identify the remains of guests at a banquet by their seating places around a table, after a roof had fallen in upon them and obliterated them beyond recognition. In the Classical use of the art, abstract images of a somewhat bizarre (and therefore memorable) nature were conceived that would be linked to parts of a speech and then to a well-known architectural feature of the hall in which the speech was to take place. By scanning the variety of statuary, friezes, articulated columns, or whatever, within the hall, the rhetorician skilled in the art could remember all the aspects of his speech. The hall would provide the order and a frame of reference which could be used over and over again for a complex constellation of constantly changing ideas.

The art of memory

In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prex consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prex to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benets of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the eld, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.

/pdf/a-survey-of-query-auto-completion-in-information-retrieval-3manvsrl87.pdf

A Survey of Query Auto Completion in Information Retrieval

Increased usage of Web search engines in our daily lives means that the trustworthiness of searched results has become crucial User studies on the usage of search engines and analysis of the factors used to determine trust that users have in search results are described in this paper Based on the analysis, we developed a system to help users determine the trustworthiness of Web search results by computing and showing each returned page's topic majority, topic coverage, locality of supporting pages (ie, pages linked to each search result) and other information The measures proposed in the paper can be applied to the search of Web-based libraries or can be useful in the usage of digital library search systems

/pdf/trustworthiness-analysis-of-web-search-results-50kclsqqmp.pdf

Trustworthiness analysis of web search results

In this paper, we provide an overview of the NTCIR IMine task, which is a core task of NTCIR-11 and also a succeeding work of INTENT@NTCIR-9 and INTENT2@NTCIR-10 tasks. IMine is composed of a subtopic mining (SM) task, a document ranking (DR) task and a TaskMine (TM) pilot task. 21 groups from Canada, China, Germany, France, Japan, Korea, Spain, UK and United States registered to the task, which makes it one of the largest tasks in NTCIR-11. Finally, we receive 45 runs from 10 teams to the SM task, 25 runs from 6 groups to the DR task and 3 runs from 2 groups to the TM task. We describe the task details, annotation of results, evaluation strategies and then the official evaluation results for each subtask.

/pdf/overview-of-the-ntcir-11-imine-task-2i8uopo943.pdf

Overview of the NTCIR-11 IMine Task

We propose a method for searching coordinate terms using a traditional Web search engine. “Coordinate terms” are terms which have the same hypernym. There are several research methods that acquire coordinate terms, but they need parsed corpora or a lot of computation time. Our system does not need any preprocessing and can rapidly acquire coordinate terms for any query term. It uses a conventional Web search engine to do two searches where queries are generated by connecting the user's query term with a conjunction “OR”. It also obtains background context shared by the query term and each returned coordinate term.

Searching coordinate terms with their context from the web

We describe methods to search with a query by example in a known domain for information in an unknown domain by exploiting Web search engines. Relational search is an effective way to obtain information in an unknown field for users. For example, if an Apple user searches for Microsoft products, similar Apple products are important clues for the search. Even if the user does not know keywords to search for specific Microsoft products, the relational search returns a product name by querying simply an example of Apple products. More specifically, given a tuple containing three terms, such as (Apple, iPod, Microsoft), the term Zune can be extracted from the Web search results, where Apple is to iPod what Microsoft is to Zune. As a previously proposed relational search requires a huge text corpus to be downloaded from the Web, the results are not up-to-date and the corpus has a high construction cost. We introduce methods for relational search by using Web search indices. We consider methods based on term co-occurrence, on lexico-syntactic patterns, and on combinations of the two approaches. Our experimental results showed that the combination methods got the highest precision, and clarified the characteristics of the methods.

Query by analogical example: relational search using web search engine indices

In this paper, we provide an overview of the NTCIR-12 IMine-2 task, which is a core task of NTCIR-12 and also a succeeding work of IMine@NTCIR-11, INTENT-2@NTCIR-10, and INTENT@NTCIR-9 tasks. IMine-2 comprises the Query Understanding subtask and the Vertical Incorporating subtask. 23 groups from diverse countries including China, France, India, Portugal, Ireland, and Japan registered to the task. Finally, IMine2 attracted 9 participating teams; we received 42 runs for the Query Understanding subtask and 15 runs for the Vertical Incorporating subtask. We describe the subtasks, data, evaluation methods, and report the official results for each subtask.

/pdf/overview-of-the-ntcir-12-imine-2-task-47vrbm2ziv.pdf

Hiroaki Ohshima

Papers

Trustworthiness analysis of web search results

Overview of the NTCIR-11 IMine Task

Searching coordinate terms with their context from the web

Query by analogical example: relational search using web search engine indices

Overview of the NTCIR-12 IMine-2 Task.