varunr14/newsummary huggingface dataset:
Questionnaires are commonly used to measure attitudes toward systems and perceptions of search experiences . Whilst the face validity of such measures has been established through repeated use in information retrieval research their reliability and wider validity are not typically examined this threatens internal validity . The evaluation of self report questionnaires is important not only for the internal validity of studies and by extension increased confidence in the results but also for examining constructs of interest over time and across different domains and systems . In this paper we look at a specific questionnaire the User Engagement Scale for its robustness as a measure . We describe three empirical studies conducted in the online news domain and investigate the reliability and validity of the UES . Our results demonstrate good reliability of the UES sub scales however we argue that a four factor structure may be more appropriate than the original six factor structure proposed in earlier work . In addition we found evidence to suggest that the UES can differentiate between systems and experimental conditions . 


Summary: We examined the robustness of the User Engagement Scale UES . Three studies were conducted in Canada and the United Kingdom. The UES sub scales were reliable across three samples of online news browser. A four factor structure rather than six may be more appropriate. The UES differentiated between online news sources and experimental conditions.
The widespread availability of the Internet and the variety of Internet based applications have resulted in a significant increase in the amount of web pages . Determining the behaviors of search engine users has become a critical step in enhancing search engine performance . Search engine user behaviors can be determined by content based or content ignorant algorithms . Although many content ignorant studies have been performed to automatically identify new topics previous results have demonstrated that spelling errors can cause significant errors in topic shift estimates . In this study we focused on minimizing the number of wrong estimates that were based on spelling errors . We developed a new hybrid algorithm combining character n gram and neural network methodologies and compared the experimental results with results from previous studies . For the FAST and Excite datasets the proposed algorithm improved topic shift estimates by 6.987 and 2.639 respectively . Moreover we analyzed the performance of the character n gram method in different aspects including the comparison with Levenshtein edit distance method . The experimental results demonstrated that the character n gram method outperformed to the Levensthein edit distance method in terms of topic identification . 


Summary: We used the character n gram method to predict topic changes in search engine queries. We obtained more successful estimations than previous studies and made remarkable contributions. We compared the character n gram method with the Levenshtein edit distance method. We analyzed ASPELL Google and Bing search engines as pre processed spelling correction methods. We conclude that Google could be used as a pre processed spelling correction method.
Because individual interpretations of the analytic hierarchy process linguistic scale vary for each user this study proposes a novel framework that AHP decision makers can use to generate numerical scales individually based on the 2 tuple linguistic modeling of AHP scale problems . By using the concept of transitive calibration individual characteristics in understanding the AHP linguistic scale are first defined . An algorithm is then proposed for detecting the individual characteristics from the linguistic pairwise comparison data that is associated with each of the AHP individual decision makers . Finally a nonlinear programming model is proposed to generate individual numerical scales that optimally match the obtained individual characteristics . Two well known numerical examples are re examined using the proposed framework to demonstrate its validity . 


Summary: This paper discusses the individual choice of numerical scale in AHP. We present an algorithm to obtain the linguistic individual characteristics in AHP. We set individual numerical scales to optimally match the individual characteristics.
This paper proposes a novel query expansion method to improve accuracy of text retrieval systems . Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words . Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model . We compared our method with other baseline query expansion schemes and methods . Evaluations performed on TREC 8 demonstrated the effectiveness of the proposed method with respect to the baseline . 


Summary: A graph of terms can be effectively used for query expansion. Such a graph is extracted from documents thanks to a LDA based methodology. Proposed method achieves good performances on standard datasets.
This investigation deals with the problem of language identification of noisy texts which could represent the primary step of many natural language processing or information retrieval tasks . Language identification is the task of automatically identifying the language of a given text . Although there exists several methods in the literature their performances are not so convincing in practice . In this contribution we propose two statistical approaches the high frequency approach and the nearest prototype approach . In the first one 5 algorithms of language identification are proposed and implemented namely character based identification word based identification special characters based identification sequential hybrid algorithm and parallel hybrid algorithm . In the second one we use 11 similarity measures combined with several types of character N Grams . For the evaluation task the proposed methods are tested on forum datasets containing 32 different languages . Furthermore an experimental comparison is made between the proposed approaches and some referential language identification tools such as LIGA NTC Google translate and Microsoft Word . Results show that the proposed approaches are interesting and outperform the baseline methods of language identification on forum texts . 


Summary: This investigation deals with the problem of language identification of noisy texts. Two statistical approaches are proposed High Frequency Approach and Nearest Prototype Approach. The proposed methods are evaluated on forum datasets containing 32 different languages. An experimental comparison is made with LIGA NTC Google translate and Microsoft Word. Results show that the proposed approaches are interesting in language identification of forum texts.
Probabilistic topic models are unsupervised generative models which model document content as a two step generation process that is documents are observed as mixtures of latent concepts or topics while topics are probability distributions over vocabulary words . Recently a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings . Novel topic models have been designed to work with parallel and comparable texts . We define multilingual probabilistic topic modeling and present the first full overview of the current research methodology advantages and limitations in MuPTM . As a representative example we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA . We provide a thorough overview of this representative multilingual model from its high level modeling assumptions down to its mathematical foundations . We demonstrate how to use the data representation by means of output sets of per topic word distributions and per document topic distributions coming from a multilingual probabilistic topic model in various real life cross lingual tasks involving different languages without any external language pair dependent translation resource cross lingual event centered news clustering cross lingual document classification cross lingual semantic similarity and cross lingual information retrieval . We also briefly review several other applications present in the relevant literature and introduce and illustrate two related modeling concepts topic smoothing and topic pruning . In summary this article encompasses the current research in multilingual probabilistic topic modeling . By presenting a series of potential applications we reveal the importance of the language independent and language pair independent data representations by means of MuPTM . We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross lingual topics that is how to effectively employ learned per topic word distributions and per document topic distributions of any multilingual probabilistic topic model in various cross lingual applications . 


Summary: A systematic overview of multilingual probabilistic topic modeling MuPTM . A tutorial on methodology modeling training output inference and evaluation of MuPTM. Language independent and language pair independent data representations. A model independent framework and applications in various cross lingual tasks. A complete MuPTM based framework for cross lingual semantic similarity.
The uncertainty children experience when searching for information influences their information seeking behavior by stimulating curiosity or hindering their search efforts . This study explored the interactions and the usability of various search interfaces and the enjoyment or uncertainty experienced by children when using them . Structural Equation Modeling was used to determine whether children feel uncertainty or a sense of control when using virtual game like interfaces to search for information associated with entertainment or as a means to satisfy an assigned learning task . We then analyzed the weight relationships among three latent variables using statistical analysis . Our results indicate that children prefer using a retrieval interface with situated affordance to satisfy entertainment related information needs as opposed to searching for information to solve specific problems . Furthermore their perceptions of text and graphic icons determined the degree to which they experienced a sense of uncertainty or control . When searching for entertainment related information they were better able to deal with uncertainty and sought greater control in their search interface compared to when they were searching for information related to assigned tasks . According to their information needs children may regard a game like interface as a toy or a tool for learning . The results of this study can serve as reference for the future development of information search interfaces aimed at arousing the interest of children . The use of virtual game like interfaces to guide the IS behavior of children warrants further study . 


Summary: Elucidation of children s info seeking behaviors using virtual retrieval interfaces. The use of SEM to explore the relationship between fun and uncertainty. Children prefer a virtual retrieval interface when at play. Text sound channels are less definite but easier to use than icon channels. Children at play are willing to endure a high degree of uncertainty control.
In this paper we investigate the impact of emotions on author profiling concretely identifying age and gender . Firstly we propose the EmoGraph method for modelling the way people use the language to express themselves on the basis of an emotion labelled graph . We apply this representation model for identifying gender and age in the Spanish partition of the PAN AP 13 corpus obtaining comparable results to the best performing systems of the PAN Lab of CLEF . 


Summary: We take into account emotions for author profiling. We model the six basic emotions with a novel graph based approach EmoGraph . We achieve comparable results to the best performing systems of the PAN Lab of CLEF 2013.
Majority of parallel machine scheduling studies consider machine as the only resource . However in most real life manufacturing environments jobs may require additional resources such as automated guided vehicles machine operators tools pallets dies and industrial robots for their handling and processing . This paper presents a review and discussion of studies on the parallel machine scheduling problems with additional resources . Papers are surveyed in five main categories machine environment additional resource objective functions complexity results and solution methods and other important issues . The strengths and weaknesses of the literature together with open areas for future studies are also emphasized . Finally extensions of integer programming models for two main classes of related problems are given and conclusions are drawn based on computational studies . number of jobs set of jobs number of machines set of machines indices of jobs i h 1 the set of possible modes belonging to job i index of resource modes processing time of job i when processed in mode k Ki processing time of job i on machine j when processed in mode k Ki the size of the single additional resource type the earliest time at which job i can start its processing i.e . release time due date of job i weight of job i denoting the importance of job i relative to other jobs completion time of job i maximum completion time of all jobs in the system i.e . makespan tardiness of job i equals to 1 if job i is tardy 0 otherwise maximum lateness 


Summary: We review the literature on parallel machine scheduling with additional resources. We structure the review around five categories. We provide both the strengths and weaknesses of the studies together with open areas. We propose several extensions on some of the mathematical models given in the literature. We conclude with several remarks based on our computational studies and the review.
Several major environmental issues like biodiversity loss and climate change currently concern the international community . These topics that are related to the development of human societies have become increasingly important since the United Nations Conference on Environment and Development or Earth Summit in Rio de Janeiro in 1992 . In this article we are interested in the first issue . We present here many examples of the help that using mathematical programming can provide to decision makers in the protection of biodiversity . The examples we have chosen concern the selection of nature reserves the control of adverse effects caused by landscape fragmentation including the creation or restoration of biological corridors the ecological exploitation of forests the control of invasive species and the maintenance of genetic diversity . Most of the presented models are or can be approximated with linear quadratic or fractional integer formulations and emphasize spatial aspects of conservation planning . Many of them represent decisions taken in a static context but temporal dimension is also considered . The problems presented are generally difficult combinatorial optimization problems some are well solved and others less well . Research is still needed to progress in solving them in order to deal with real instances satisfactorily . Moreover relations between researchers and practitioners have to be strengthened . Furthermore many recent achievements in the field of robust optimization could probably be successfully used for biodiversity protection a domain in which many data are uncertain . 


Summary: We review representative optimization problems arising in biodiversity protection. They concern e.g. nature reserves landscape fragmentation and genetic diversity. Some of these problems come from the literature and others are new. For each problem we present one or several mathematical programming formulations. Some computational results are reported.
The intention gap between users and queries results in ambiguous and broad queries . To solve these problems subtopic mining has been studied which returns a ranked list of possible subtopics according to their relevance popularity and diversity . This paper proposes a novel method to mine subtopics using simple patterns and a hierarchical structure of subtopic candidates . First relevant and various phrases are extracted as subtopic candidates using simple patterns based on noun phrases and alternative partial queries . Second a hierarchical structure of the subtopic candidates is constructed using sets of relevant documents from a web document collection . Finally the subtopic candidates are ranked considering a balance between popularity and diversity using this structure . In experiments our proposed methods outperformed the baselines and even an external resource based method at high ranked subtopics which shows that our methods can be effective and useful in various search scenarios like result diversification . 


Summary: We use only web document collection instead of query logs and external resources. Our simple patterns are based on noun phrases and alternative partial queries. We maintain a balance between popularity and diversity of subtopics. Our method covered various search intentions of a query by its few subtopics. Our results were steadily improved by extracting more relevant and various subtopics.
Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both commercial and public sectors . In this paper we present SentiCircles a lexicon based approach for sentiment analysis on Twitter . Different from typical lexicon based approaches which offer a fixed and static prior sentiment polarities of words regardless of their context SentiCircles takes into account the co occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre assigned strength and polarity in sentiment lexicons accordingly . Our approach allows for the detection of sentiment at both entity level and tweet level . We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments . Results show that our approach significantly outperforms the baselines in accuracy and F measure for entity level subjectivity and polarity detections . For tweet level sentiment detection our approach performs better than the state of the art SentiStrength by 4 5 in accuracy in two datasets but falls marginally behind by 1 in F measure in the third dataset . 


Summary: We propose a semantic sentiment representation of words called SentiCircle. SentiCircle captures the contextual semantic of words from their co occurrences. SentiCircle updates the sentiment of words based on their contextual semantics. SentiCircle can be used to perform entity and tweet level level sentiment analysis.
Time is an important aspect of text documents . While some documents are atemporal many have strong temporal characteristics and contain contents related to time . Such documents can be mapped to their corresponding time periods . In this paper we propose estimating the focus time of documents which is defined as the time period to which document s content refers and which is considered complementary dimension to the document s creation time . We propose several estimators of focus time by utilizing statistical knowledge from external resources such as news article collections . The advantage of our approach is that document focus time can be estimated even for documents that do not contain any temporal expressions or contain only few of them . We evaluate the effectiveness of our methods on the diverse datasets of documents about historical events related to 5 countries . Our approach achieves average error of less than 21years on collections of Wikipedia pages extracts from history related books and web pages while using the total time frame of 113years . We also demonstrate an example classification method to distinguish temporal from atemporal documents . 


Summary: Statistical approach for estimating the focus time of text documents. Classification framework for categorizing documents into temporal and atemporal. Bi Temporal Document Representation using document focus time and creation time.
The task of finding groups or teams has recently received increased attention as a natural and challenging extension of search tasks aimed at retrieving individual entities . We introduce a new group finding task given a query topic we try to find knowledgeable groups that have expertise on that topic . We present five general strategies for this group finding task given a heterogenous document repository . The models are formalized using generative language models . Two of the models aggregate expertise scores of the experts in the same group for the task one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic whilst the remaining two models directly estimate the degree to which a group is a knowledgeable group for a given topic . For evaluation purposes we construct a test collection based on the TREC 2005 and 2006 Enterprise collections and define three types of ground truth for our task . Experimental results show that our five knowledgeable group finding models achieve high absolute scores . We also find significant differences between different ways of estimating the association between a topic and a group . 


Summary: We introduce a new information retrieval task given a topic try to find knowledgeable groups that have expertise on the topic. Five probabilistic language models are proposed to tackle the challenge of automatically finding groups of experts in heterogeneous document collections. For evaluation purpose a data set is created based on a publicly downloadable corpus used in the TREC Enterprise 2005 and 2006 tracks and three types of ground truth are defined. We provide a detailed analysis of the performance of the proposed group finding models.
Research into unsupervised ways of stemming has resulted in the past few years in the development of methods that are reliable and perform well . Our approach further shifts the boundaries of the state of the art by providing more accurate stemming results . The idea of the approach consists in building a stemmer in two stages . In the first stage a stemming algorithm based upon clustering which exploits the lexical and semantic information of words is used to prepare large scale training data for the second stage algorithm . The second stage algorithm uses a maximum entropy classifier . The stemming specific features help the classifier decide when and how to stem a particular word . In our research we have pursued the goal of creating a multi purpose stemming tool . Its design opens up possibilities of solving non traditional tasks such as approximating lemmas or improving language modeling . However we still aim at very good results in the traditional task of information retrieval . The conducted tests reveal exceptional performance in all the above mentioned tasks . Our stemming method is compared with three state of the art statistical algorithms and one rule based algorithm . We used corpora in the Czech Slovak Polish Hungarian Spanish and English languages . In the tests our algorithm excels in stemming previously unseen words . Moreover it was discovered that our approach demands very little text data for training when compared with competing unsupervised algorithms . 


Summary: New unsupervised stemming algorithm is introduced in this article. The algorithm exploits lexical as well as semantic information of words. Performance of stemming is measured on several languages Czech Slovak Polish Hungarian Spanish and English . We outperform competing stemmers in inflection removal test information retrieval task and language modeling task.