waashk/dblp huggingface dataset:
passage relevance models for genomics search. we present a passage relevance model for integrating semantic and statistical evidence of biomedical concepts and topics using a probabilistic graphical model. component models of topics, concepts, terms, and document are represented as potential functions within a markov random field. the probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. relevance model feedback of top ranked passages is used to improve distributional estimates of concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. by integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.
learning latent semantic relations from clickthrough data for query suggestion. for a given query raised by a specific user, the query suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. due to the complexity of the web structure and the ambiguity of users' inputs, most of the suggestion algorithms suffer from the problem of poor recommendation accuracy. in this paper, aiming at providing semantically relevant queries for users, we develop a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two bipartite graphs (user-query and query-url bipartite graphs) extracted from the clickthrough data. based on this, we first propose a joint matrix factorization method which utilizes two bipartite graphs to learn the low-rank query latent feature space, and then build a query similarity graph based on the features. after that, we design an online ranking algorithm to propagate similarities on the query similarity graph, and finally recommend latent semantically relevant queries to users. experimental analysis on the clickthrough data of a commercial search engine shows the effectiveness and the efficiency of our method.
trust, authority and popularity in social information retrieval. we present a social information retrieval (sir) model comprising the social network of actors (e.g., authors, publishers, consumers), the graph representing relations in data (e.g., publications), and the links between the social and data network that reflect activities in the network such as search, authoring, annotation, etc. building on this hybrid network, we describe relevance in terms of the trust propagated through the network and rendered onto a given item. in particular, relevance is a function of the approval votes from the associated sub-graph and the reputation of the sub-graph nodes. we explore a model that differentiates between approval from actors who are perceived authorities by the user and the approval by a wider community, representing the popular opinion.
discovering leaders from community actions. we introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. in particular, we consider social networks where users perform actions. actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in yahoo! music, or movies as in yahoo! movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. the assumption is that actions performed by a user can be seen by their network friends. users seeing their friends' actions are sometimes tempted to perform those actions. we are interested in the problem of studying the propagation of such "influence", and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. we consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. we run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. the results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. to the best of our knowledge, this the first frequent pattern based approach to social network mining.
a random walk on the red carpet: rating movies with user reviews and pagerank. although pagerank has been designed to estimate the popularity of web pages, it is a general algorithm that can be applied to the analysis of other graphs other than one of hypertext documents. in this paper, we explore its application to sentiment analysis and opinion mining: i.e. the ranking of items based on user textual reviews. we first propose various techniques using collocation and pivot words to extract a weighted graph of terms from user reviews and to account for positive and negative opinions. we refer to this graph as the sentiment graph. using pagerank and a very small set of adjectives (such as 'good', 'excellent', etc.) we rank the different items. we illustrate and evaluate our approach using reviews of box office movies by users of a popular movie review site. the results show that our approach is very effective and that the ranking it computes is comparable to the ranking obtained from the box office figures. the results also show that our approach is able to compute context-dependent ratings.
answering questions with authority. this paper presents a novel approach to textual question answering (qa) which identifies answers to natural language questions by leveraging large collections of question-answer pairs extracted from web sources or generated automatically from text. instead of using the traditional retrieve-and-rerank approach common to most previous approaches to factoid question answering, we introduce a new model of answer authority which allows question-answering systems to estimate the quality of answers not just in isolation - but in the larger context of the information contained in the corpus as a whole. our approach in this paper hinges on the creation of a new representation of the information stored in a document collection, known as a question-answer database (quab).we assume that a quab represents a weighted directed graph consisting of the set of factoid question-answer pairs (qap) that can be asked - and answered - given the content of a corpus. once a set of qap have been generated, we use inferential relationships identified by a system for recognizing textual entailment in order to construct the link structure necessary to compute the authority of each interconnected question or answer. we have found access to the quab graph not only improves the accuracy of current answer retrieval techniques, but also allows for the determination of when no valid answer can be found for a question in a text corpus. experimental results show that the authority derived from a graph of question-answer pairs can increase the performance of a factoid qa system by nearly 30%.
table summarization with the help of domain lattices. table summarization is necessary in various scenarios where it is hard to display a large table. it can benefit from knowledge about acceptable value clustering alternatives. in this paper, we formulate the problem of table summarization with the help of domain knowledge lattices. we provide the outline of a fuzzy mechanism to express alternative clustering strategies. we further sketch a novel ranked set cover based evaluation mechanism (rsc) to tackle with the inherent complexity.
a heuristic approach for checking containment of generalized tree-pattern queries. query processing techniques for xml data have focused mainly on tree-pattern queries (tpqs). however, the need for querying xml data sources whose structure is very complex or not fully known to the user, and the need to integrate multiple xml data sources with different structures have driven, recently, the suggestion of query languages that relax the complete specification of a tree pattern. in order to implement the processing of such languages in current dbmss, their containment problem has to be efficiently solved. in this paper, we consider a query language which generalizes tpqs by allowing the partial specification of a tree pattern. partial tree-pattern queries (ptpqs) constitute a large fragment of xpath that flexibly permits the specification of a broad range of queries from keyword queries without structure, to queries with partial specification of the structure, to complete tpqs. we address the containment problem for ptpqs. this problem becomes more complex in the context of ptpqs because the partial specification of the structure allows new, non-trivial, structural expressions to be inferred from those explicitly specified in a query. we show that the containent problem cannot be characterized by homomorphisms between ptpqs, even when ptpqs are put in a canonical form that comprises all derived structural expressions. we provide necessary and sufficient conditions for this problem in terms of homomorphisms between ptpqs and (a possibly exponential number of) tpqs. to cope with the high complexity of ptpq containment, we suggest a heuristic approach for this problem that trades accuracy for speed. an extensive experimental evaluation of our heuristic shows that our heuristic approach can be efficiently implemented in a query optimizer.
semi-automated logging of contact center telephone calls. modern businesses use contact centers as a communication channel with users of their products and services. the largest factor in the expense of running a telephone contact center is the labor cost of its agents. ibm research has built a new system, contact-center agent buddies (cab), which is designed to help reduce the average handle time (aht) for customer calls, thereby also reducing their cost. in this paper, we focus on the call logging subsystem, which helps agents reduce the time they spend documenting those calls. we built a template cab and a call logging cab, using a pipeline consisting of audio capture of a telephone conversation, automatic speech recognition, text analysis, and log generation. we developed techniques for asr text cleansing, including normalization of expressions and acronyms, domain terms, capitalization, and boundaries for sentences, paragraphs, and call segments. we found that simple heuristics suffice to generate high-quality logs from the normalized sentences. the pipeline yields a candidate call log which the agents can edit in less time than it takes them to generate call logs manually. evaluation of the call logging cab in an industrial contact center environment shows that it reduces the amount of time agents spend logging calls by at least 50% without compromising the quality of the resulting call documentation.
coreference resolution using expressive logic models. coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. however, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. in this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the markov logic network. the proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.
adaptive distributed indexing for structured peer-to-peer networks. structured peer-to-peer networks support keyword search by building a distributed index over the collective content shared by all peers. building the index and processing queries involve data transfer among peers, thus it is important to keep both of these activities bandwidth-efficient. however, this goal is difficult to attain, as smaller, less precise indices reduce index building and access costs but increase query processing cost, which potentially increases overall cost. we study the trade-off between indexing cost and query processing cost in a structured peer-to-peer network and propose a cost-reducing, adaptive, distributed indexing technique based on the term distributions in local shared contents and user query logs. using this information, we reduce costs by tuning the precision of the index. the approach we take is to group local documents and to index the groups instead of either individual documents or entire peer collections. we control total cost by controlling the number and contents of groups. we propose a probabilistic model to estimate the cost of grouping, which allows us to identify the optimal number of groups to be created. in addition, we propose a cost-based distance function to guide the document grouping process. experimental results show that our adaptive indexing technique reduces cost by up to 47% compared with peer-level grouping and by up to 73% compared with document-level grouping.
suppressing outliers in pairwise preference ranking. many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isolation, document pairs are used as instances in the learning process. one disadvantage of this process is that a noisy relevance judgment on a single document can lead to a large number of mis-labeled document pairs. this can jeopardize robustness and deteriorate overall ranking performance. in this paper we study the effects of outlying pairs in rank learning with pairwise preferences and introduce a new meta-learning algorithm capable of suppressing these undesirable effects. this algorithm works as a second optimization step in which any linear baseline ranker can be used as input. experiments on eight different ranking datasets show that this optimization step produces statistically significant performance gains over state-of-the-art methods.
a latent variable model for query expansion using the hidden markov model. we propose a novel probabilistic method based on the hidden markov model(hmm) to learn the structure of a latent variable model (lvm) for query language modeling. in the proposed lvm, the combinations of query terms are viewed as the latent variables and the segmented chunks from the feedback documents are used as the observations given these latent variables. our extensive experiments shows that our method significantly outperforms a number of strong baselines in terms of both effectiveness and robustness.
relating dependent indexes using dempster-shafer theory. traditional information retrieval (ir) approaches assume that the indexing terms are independent, which is not true in reality. although some previous studies have tried to consider term relationships, strong simplifications had to be made at the very basic indexing step, namely, dependent terms are assigned independent counts or probabilities. in this study, we propose to consider dependencies between terms using dempster-shafer theory of evidence. an occurrence of a string in a document is considered to represent the set of all the terms implied in it. probability is assigned to such a set of terms instead of individual terms. during query evaluation phase, a part of the probability of a set can be transferred to those of the query that are related, allowing us to integrate language-dependent relations in ir. this approach has been tested on several chinese ir collections. our experimental results show that our model can outperform the existing state-of-the-art approaches. the proposed method can be used as a general way to consider different types of relationship between terms and for other languages.
privacy-preserving data publishing for horizontally partitioned databases. there is an increasing need for sharing data repositories containing personal information across multiple distributed, possibly untrusted, and private databases. such data sharing is subject to constraints imposed by privacy of data subjects as well as data confidentiality of institutions or data providers. we developed a set of decentralized protocols that enable data sharing for horizontally partitioned databases given these constraints. our approach includes a distributed anonymization protocol that allows independent data providers to build a virtual anonymized database, and a distributed querying protocol that allows clients to query the virtual database.
integrating clustering and multi-document summarization to improve document understanding. document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. current document clustering methods usually represent documents as a term-document matrix and perform clustering algorithms on it. although these clustering methods can group the documents satisfactorily, it is still hard for people to capture the meanings of the documents since there is no satisfactory interpretation for each document cluster. in this paper, we propose a new language model to simultaneously cluster and summarize the documents. by utilizing the mutual influence of the document clustering and summarization, our method makes (1) a better document clustering method with more meaningful interpretation and (2) a better document summarization method taking the document context information into consideration.
road: an efficient framework for location dependentspatial queries on road networks. in this research, we develop road, a system framework for processing location dependent spatial queries (ldsqs) that search for spatial objects of interest on road networks. by exploiting search space pruning, road is very efficient and flexible for various ldsqs on different types of objects over large-scale networks. in road, a large road network is organized as a set of interconnected regional sub-networks (called rnets) augmented with 1) shortcuts for accelerating search traversals; and 2) object abstracts for guiding object search. in this poster, we outline this framework and explain how it can support efficient location-dependent nearest neighbor search.
tinylex: static n-gram index pruning with perfect recall. inverted indexes using sequences of characters (n-grams) as terms provide an error-resilient and language-independent way to query for arbitrary substrings and perform approximate matching in a text, but present a number of practical problems: they have a very large number of terms, they exhibit pathologically expensive worst-case query times on certain natural inputs, and they cannot cope with very short query strings. in word-based indexes, static index pruning has been successful in reducing index size while maintaining precision, at the expense of recall. taking advantage of the unique inclusion structure of n-gram terms of different lengths, we show that the lexicon size of an n-gram index can be reduced by 7 to 15 times without any loss of recall, and without any increase in either index size or query time. because the lexicon is typically stored in main memory, this substantially reduces the memory required for queries. simultaneously, our construction is also the first overlapping n-gram index to place tunable worst-case bounds on false positives and to permit efficient queries on strings of any length. using this construction, we also demonstrate the first feasible n-gram index using words rather than characters as units, and its applications to phrase searching.
on effective presentation of graph patterns: a structural representative approach. in the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. however, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. currently, what the state-of-art techniques give is a lengthy list of exact patterns, which are undesirable in the following two aspects: (1) on the micro side, due to various inherent noises or data diversity, exact patterns are usually not too useful in many real applications; and (2) on the macro side, the rigid structural requirement being posed often generates an excessive amount of patterns that are only slightly different from each other, which easily overwhelm the users. in this paper, we study the presentation problem of graph patterns, where structural representatives are deemed as the key mechanism to make the whole strategy effective. as a solution to fill the usability gap, we adopt a two-step smoothing-clustering framework, with the first step adding error tolerance to individual patterns (the micro side), and the second step reducing output cardinality by collapsing multiple structurally similar patterns into one representative (the macro side). this novel, integrative approach is never tried in previous studies, which essentially rolls-up our attention to a more appropriate level that no longer looks into every minute detail. the above framework is general, which may apply under various settings and incorporate a lot of extensions. empirical studies indicate that a compact group of informative delegates can be achieved on real datasets and the proposed algorithms are both efficient and scalable.
measuring user preference changes in digital libraries. much research has been conducted using web access logs to study implicit user feedback and infer user preferences from clickstreams. however, little research measures the changes of user preferences of ranking documents over time. we present a study that measures the changes of user preferences based on an analysis of access logs of a large scale digital library over one year. a metric based on the accuracy of predicting future user actions is proposed. the results show that although user preferences change over time, the majority of user actions should be predictable from previous browsing behavior in the digital library.
wildcards for lightweight information integration in virtual desktops. we present a flexible information integration approach which addresses the dynamic integration needs in a personal desktop environment where only partial mappings are defined between the sources to be integrated. our approach is based on query rewriting using substitution rules. in addition to exploiting defined mappings, we employ substitution strategies, which are inspired by the idea of using wildcards in querying and filtering tasks. starting from a triple based query language as used for querying rdf data, unmapped ontological elements are substituted in a controlled way with variables, leading to a controlled form of query relaxation. in addition, the approach also provides evidences for refining the existing mapping based on the results of executing the relaxed queries. different strategies for replacing non-matched ontology elements with variables are presented and evaluated over real-world data sets.