Short Queries Research Articles

Hybrid Approach for Query Expansion using Query Log

Web search users usually submit short and ambiguous queries to specify their requirement. In order to improve performance of short and ambiguous queries, query expansion is used. Query expansion is as an effective way to improve the performance of information retrieval systems by adding relevant terms to the original query. After using search engine lots of data get accumulated, from which queries that have been used to retrieve documents are used. This data is stored as query log. These query logs provide valuable information to extract relationships between queries and documents that can be used in query expansion. This paper proposes method first to determine ambiguous queries using Kullback leibler distance model. It measures difference between two probability distributions. Second, relevant or most suitable expansion terms are selected from the documents with the analysis of relation between queries and documents. The relation can be evaluated by calculating frequency co-efficient with respect to document and document collection.

Exploiting social bookmarking services to build clustered user interest profile for personalized search

Search engine users tend to write short queries, generally comprising of two or three query words. As these queries are often ambiguous or incomplete, search engines tend to return results whose rankings reflect a community of intent. Moreover, search engines are designed to satisfy the needs of the general populace, not those of a specific searcher. To address these issues, we propose two methods that use Singular Value Decomposition (SVD) to build a Clustered User Interest Profile (CUIP), for each user, from the tags annotated by a community of users to web resources of interest. A CUIP consists of clusters of semantically or syntactically related tags, each cluster identifying a topic of the user’s interest. The matching cluster, to the given user’s query, aids in disambiguation of user search needs and assists the search engine to generate a set of personalized search results. A series of experiments was executed against two data sets to judge the clustering tendency of the cluster structure CUIP, and to evaluate the quality of personalized search. The experiment results indicate that the CUIP based personalized search outperforms the baseline search and is better than the other approaches that use social bookmarking services for building a user profile and use it for personalized search.

Using Personalization to Improve XML Retrieval

As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, re-ranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.

Exploiting entity relationship for query expansion in enterprise search

Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain.

Studying and analysing students web search behaviours within three Jordanian universities

The World Wide Web has become a phenomenon. The web is a crucial source of information for almost every person. Peoples with different jobs using the internet to meet their professional needs, and the university students constitute a large segment of those people who use the internet frequently to accomplish their information needs. This research has conducted an investigation to determine how Jordanian university students are using the web, what are the characteristics of queries used when searching the internet, what are the top searched queries, …, etc. All these goals are achieved after extracting different queries and information from these web log files and analysing them. Results have showed that students are interested in topics related to entertainment and society more than topics related to their studies. Students submit short queries consisting of 1, 2, or 3 terms with little usage of different query operators. Unsurprisingly, they use Google as the preferred search engine and Arabic as the preferred search language.

EPA-0544 - Dementia in patients with obsessive compulsive disorder (OCD)

Introduction Despite the high prevalence of obsessive-compulsive symptoms located around 2-3% of the population, there continue to be cases where the characteristics of the patient or the circumstances of their environment, they fall short queries mental health or when they do not for the disorder itself, but for another reason obsessional symptoms worsen. Objectives Expose using clinical case, the existence of patients with obsessive pathology whose characteristics do not seek mental health consultation, until this is associated with a new disease that interferes significantly in vital organization. Results We report the case of a man of 88 years old, married at 60, was admitted to the psychiatric consultation at the request of his wife 29 years his junior, for behavioral disorders several years of evolution and history of obsessive symptoms compulsive, which did not interfere with their daily lives by the lack of insight and poor social environment Conclusions OCD is included in anxiety disorders. It is characterized by the presence of obsessions and compulsions that interfere with personal, work and / or patient's social. There are cases that own personality traits of the patient, this disorder is not diagnosed early and choose to go only when associated with worsening cognitive impairment rituals and interfere with family life.

Detecting verbose queries and improving information retrieval

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.

Improving the performance of pipelined query processing with skipping—and its comparison to document-wise partitioning

Web search engines need to provide high throughput and short query latency. Recent results show that pipelined query processing over a term-wise partitioned inverted index may have superior throughput. However, the query processing latency and scalability with respect to the collections size are the main challenges associated with this method. In this paper, we evaluate the effect of inverted index skipping on the performance of pipelined query processing. Further, we introduce a novel idea of using Max-Score pruning within pipelined query processing and a new term assignment heuristic, partitioning by Max-Score. Our current results indicate a significant improvement over the state-of-the-art approach and lead to several further optimizations which include dynamic load balancing, intra-query concurrent processing and a hybrid combination between pipelined and non-pipelined execution. Lastly, we show how the state of term-wise partitioning relates to the industry standard document-wise partitioning. Even though there are situations pipelined query processing is advantegous, document-wise partitioning is still the road to follow.

Self-Deception in the Classroom: Educational manifestations of Sartre’s concept of bad faith

This article explores an important section of Jean-Paul Sartre’s famous early work, Being and Nothingness. In that section Sartre proposes that part of the human condition is to actively engage in a particular kind of self-deception he calls bad faith. Bad faith is recognized by the obvious inconsistency between the purported self-knowledge of an individual and ways of acting and being in the world that are demonstrably in defiance of that stated position. This article begins by exploring examples of this self-deception in education along with a short query into the role pain avoidance might play in the development of this particular skill. The article then goes on to offer a series of manifestations of bad faith that Sartre identified along with possible examples of how each example, perhaps ‘excuse’, of bad faith might appear in education.

Searching for specific health‐related information in MedlinePlus: Behavioral patterns and user experience

Searches for specific factual health information constitute a significant part of consumer health information requests, but little is known about how users search for such information. This study attempts to fill this gap by observing users' behavior while using MedlinePlus to search for specific health information. Nineteen students participated in the study, and each performed 12 specific tasks. During the search process, they submitted short queries or complete questions, and they examined less than 1 result per search. Participants rarely reformulated queries; when they did, they tended to make a query more specific or more general, or iterate in different ways. Participants also browsed, primarily relying on the alphabetical list and the anatomical classification, to navigate to specific health topics. Participants overall had a positive experience with MedlinePlus, and the experience was significantly correlated with task difficulty and participants' spatial abilities. The results suggest that, to better support specific item search in the health domain, systems could provide a more “natural” interface to encourage users to ask questions; effective conceptual hierarchies could be implemented to help users reformulate queries; and the search results page should be reconceptualized as a place for accessing answers rather than documents. Moreover, multiple schemas should be provided to help users navigate to a health topic. The results also suggest that users' experience with information systems in general and health‐related systems in particular should be evaluated in relation to contextual factors, such as task features and individual differences.

Identify the User’s Information Need Using the Current Search Context

This paper proposes a solution to a problem frequently asked in the field of the information search on the Web that is the ambiguity of user queries. The exploitation of the current search context of the user in order to identify his information need which is expressed often through short and ambiguous queries constitutes the main contribution of this work. New contextual dimensions are used that are the user’s recent interests and the temporal events that may influence his browsing behavior. The modeling of the user’s browsing behavior taking into account the proposed dimensions gave encouraging preliminary results.

Wikimantic: Toward effective disambiguation and expansion of queries

This paper presents an implemented and evaluated methodology for disambiguating terms in search queries and for augmenting queries with expansion terms. By exploiting Wikipedia articles and their reference relations, our method is able to disambiguate terms in particularly short queries with few context words and to effectively expand queries for retrieval of short documents such as tweets. Our strategy can determine when a sequence of words should be treated as a single entity rather than as a sequence of individual entities. This work is part of a larger project to retrieve information graphics in response to user queries.

Ringtail

Social media nowcasting--using online user activity to describe real-world phenomena--is an active area of research to supplement more traditional and costly data collection methods such as phone surveys. Given the potential impact of such research, we would expect general-purpose nowcasting systems to quickly become a standard tool among noncomputer scientists, yet it has largely remained a research topic. We believe a major obstacle to widespread adoption is the nowcasting feature selection problem. Typical nowcasting systems require the user to choose a handful of social media objects from a pool of billions of potential candidates, which can be a time-consuming and error-prone process. We have built RINGTAIL, a nowcasting system that helps the user by automatically suggesting high-quality signals. We demonstrate that RINGTALL can make nowcasting easier by suggesting relevant features for a range of topics. The user provides just a short topic query (e.g., unemployment) and a small conventional dataset in order for RINGTALL to quickly return a usable predictive nowcasting model.

Piranha

Cluster computing has emerged as a key parallel processing platform for large scale data. All major internet companies use it as their major central processing platform. One of cluster computing's most popular examples is MapReduce and its open source implementation Hadoop. These systems were originally designed for batch and massive-scale computations. Interestingly, over time their production workloads have evolved into a mix of a small fraction of large and long-running jobs and a much bigger fraction of short jobs. This came about because these systems end up being used as data warehouses, which store most of the data sets and attract ad hoc, short, data-mining queries. Moreover, the availability of higher level query languages that operate on top of these cluster systems proliferated these ad hoc queries. Since existing systems were not designed for short, latency-sensistive jobs, short interactive jobs suffer from poor response times.In this paper, we present Piranha--a system for optimizing short jobs on Hadoop without affecting the larger jobs. It runs on existing unmodified Hadoop clusters facilitating its adoption. Piranha exploits characteristics of short jobs learned from production workloads at Yahoo! clusters to reduce the latency of such jobs. To demonstrate Piranha's effectiveness, we evaluated its performance using three realistic short queries. Piranha was able to reduce the queries' response times by up to 71%.

Finding similar queries based on query representation analysis

In order to understand user intents behind their queries, many researchers study similar query finding. Recently, the click graph has shown its utility in describing the relationship between queries and URLs. The previous approaches mainly either generate related terms or find relevant queries based on the co-clicked URLs. However, these approaches may suffer from the complexity of natural language processing and click-through data sparseness. In this paper, we tackle this problem through three query probability distribution representation models: Click Model, Term Model, and Semantic Model. The Click Model extracts credible transition probability from queries to URLs, and describes a query without considering web contents. The Term Model focuses on representing a query via term distribution over its main entities and purposes, which can better capture information needs behind short and ambiguous keyword queries. The Semantic Model learns potential intent distribution of queries to distinguish user intents behind a query. Among the three models, we apply pairwise similarity metrics and graph-based personalized pagerank to find similar queries. Compared to traditional representation models, our representation models are verified to be effective and efficient, especially for long tail queries.

Effectiveness of search result classification based on relevance feedback

Relevance feedback (RF) has been studied under laboratory conditions using test collections and either test persons or simple simulation. These studies have given mixed results. Automatic (or pseudo) RF and intellectual RF, both leading to query reformulation, are the main approaches to explicit RF. In the present study we perform RF with the help of classification of search results. We conduct our experiments in a comprehensive collection, namely various TREC ad-hoc collections with 250 topics. We also studied various term space reduction techniques for the classification process. The research questions are: given RF on top results of pseudo RF (PRF) query results, is it possible to learn effective classifiers for the following results? What is the effectiveness of various classification methods? Our findings indicate that this approach of applying RF is significantly more effective than PRF with short (title) queries and long (title and description) queries.

One example based action detection in hough space

Given a short action query video, to detect the same category action in a target video is a very important research topic. We propose a fast action detection method motivated by the idea of Hough Transformation. First, we extract the HOG features at the corner points from the query video. The corner points are referred to as interest points. Then, video clips are formed by sliding a window on the query video. For each T frames of a clip, in the displacement Hough space, the interest points in all of the frames are matched with the interest points in the first frame. We count the matched pairs in the cells of the Hough space to form a 2d displacement histogram. The query video is represented by a 2d displacement histogram sequence. After that, we divide the target video with motion into video cubes. These video cubes are similarly represented by displacement histogram sequences. The matrix cosine similarity is used to compute the similarities between the query video and the video cubes. This process is referred to as action matching. In the end, with the action matching results, we precisely localize the action using the locations of the matched interest points. Our key contribution is that we propose a very simple and fast algorithm that represents the actions as the displacement histogram sequences. Experiments on the challenging datasets containing both of the simple and realistic backgrounds confirm the effectiveness and efficiency of our method.

Latent word context model for information retrieval

The application of word sense disambiguation (WSD) techniques to information retrieval (IR) has yet to provide convincing retrieval results. Major obstacles to effective WSD in IR include coverage and granularity problems of word sense inventories, sparsity of document context, and limited information provided by short queries. In this paper, to alleviate these issues, we propose the construction of latent context models for terms using latent Dirichlet allocation. We propose building one latent context per word, using a well principled representation of local context based on word features. In particular, context words are weighted using a decaying function according to their distance to the target word, which is learnt from data in an unsupervised manner. The resulting latent features are used to discriminate word contexts, so as to constrict query's semantic scope. Consistent and substantial improvements, including on difficult queries, are observed on TREC test collections, and the techniques combines well with blind relevance feedback. Compared to traditional topic modeling, WSD and positional indexing techniques, the proposed retrieval model is more effective and scales well on large-scale collections.

Personalizing search using socially enhanced interest model, built from the stream of user's activity

Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are ver...

Back to our roots for retrieving very short passages

AbstractThis article tackles the task of retrieving very short documents via even shorter queries. The problem on hand may relate to the retrieval of tweets, image and table captions, short text messages (SMS) and sponsored retrieval among others. In such cases, document and/or query expansion using thesauri and other external resources (e.g., Wikipedia) usually available on the World Wide Web (WWW) are proven to be effective approaches. However, the focus of this paper is on documents that are written in lesser known languages for which the WWW is of limited use. Our experiments are based on two main corpora extracted from historical manuscripts written in Latin and Middle High German. We found that retrieving very short documents whose lengths are quite similar via short queries given that no external enrichment resources are available, the classical tf‐idf model performs as satisfactorily as the more complex models do, if not better sometimes.