varunr14/newsummary huggingface dataset:
Applying natural language processing for mining and intelligent information access to tweets is a challenging emerging research area . Unlike carefully authored news text and other longer content tweets pose a number of new challenges due to their short noisy context dependent and dynamic nature . Information extraction from tweets is typically performed in a pipeline comprising consecutive stages of language identification tokenisation part of speech tagging named entity recognition and entity disambiguation . In this work we describe a new Twitter entity disambiguation dataset and conduct an empirical analysis of named entity recognition and disambiguation investigating how robust a number of state of the art systems are on such noisy texts what the main sources of error are and which problems should be further investigated to improve the state of the art . 


Summary: We analyse the named entity recognition and disambiguation performance on tweets. Multiple state of the art systems are included. Commercial and academic systems suffer the same range of problems. Lack of context is a major problem demanding new custom NER NEL approaches. A named entity linking corpus is released with the paper.
Production ramp up is an important phase in the lifecycle of a manufacturing system which still has significant potential for improvement and thereby reducing the time to market of new and updated products . Production systems today are mostly one of a kind complex engineered to order systems . Their ramp up is a complex order of physical and logical adjustments which are characterised by try and error decision making resulting in frequent reiterations and unnecessary repetitions . Studies have shown that clear goal setting and feedback can significantly improve the effectiveness of decision making in predominantly human decision processes such as ramp up . However few measurement driven decision aides have been reported which focus on ramp up improvement and no systematic approach for ramp up time reduction has yet been defined . In this paper a framework for measuring the performance during ramp up is proposed in order to support decision making by providing clear metrics based on the measurable and observable status of the technical system . This work proposes a systematic framework for data preparation ramp up formalisation and performance measurement . A model for defining the ramp up state of a system has been developed in order to formalise and capture its condition . Functionality quality and performance based metrics have been identified to formalise a clear ramp up index as a measurement to guide and support the human decision making . For the validation of the proposed framework two ramp up processes of an assembly station were emulated and their comparison was used to evaluate this work . 


Summary: We analyse the ramp up process and identify the key performance metrics. We formalise the ramp up process as a state transition model. We provide a framework for measuring the overall ramp up performance based on the identified metrics. The performance measure has been shown to improve ramp up efficiency and reduce time.
This paper considers the uncapacitated lot sizing problem with batch delivery focusing on the general case of time dependent batch sizes . We study the complexity of the problem depending on the other cost parameters namely the setup cost the fixed cost per batch the unit procurement cost and the unit holding cost . We establish that if any one of the cost parameters is allowed to be time dependent the problem is NP hard . On the contrary if all the cost parameters are stationary and assuming no unit holding cost we show that the problem is polynomially solvable in time O where T denotes the number of periods of the horizon . We also show that in the case of divisible batch sizes the problem with time varying setup costs a stationary fixed cost per batch and no unit procurement nor holding cost can be solved in time O . 


Summary: We study the lot sizing problem with time dependent batch sizes. We prove that the problem is NP hard if one of the cost parameters is time varying. The problem is polynomial for stationary cost parameters and null holding cost.
To avoid a sarcastic message being understood in its unintended literal meaning in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with a hashtag such as sarcasm . We collected a training corpus of about 406 thousand Dutch tweets with hashtag synonyms denoting sarcasm . Assuming that the human labeling is correct we train a machine learning classifier on the harvested examples and apply it to a sample of a day s stream of 2.25 million Dutch tweets . Of the 353 explicitly marked tweets on this day we detect 309 with the hashtag removed . We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag . 35 of the top 250 ranked tweets are indeed sarcastic . Analysis indicates that the use of hashtags reduces the further use of linguistic markers for signaling sarcasm such as exclamations and intensifiers . We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of non verbal expressions that people employ in live interaction when conveying sarcasm . Checking the consistency of our finding in a language from another language family we observe that in French the hashtag sarcasme has a similar polarity switching function be it to a lesser extent . 


Summary: The use of hashtags such as sarcasm reduces the further use of linguistic markers of sarcasm in tweets. Hashtags such as sarcasm appear to be the extralinguistic equivalent of non verbal expressions in live interaction. Sarcastic hashtags are 90 appropriate. Sarcastic tweets without hashtags are hard to distinguish from non sarcastic hyperbolic tweets. In French tweets the hashtag sarcasme conveys a polarity switch less frequently than in Dutch.
In this paper we study on effective and efficient processing of keyword based queries over graph databases . To produce more relevant answers to a query than the previous approaches we suggest a new answer tree structure which has no constraint on the number of keyword nodes chosen for each keyword in the query . For efficient search of answer trees on the large graph databases we design an inverted list index to pre compute and store connectivity and relevance information of nodes to keyword terms in the graph . We propose a query processing algorithm which aggregates from the pre constructed inverted lists the best keyword nodes and root nodes to find top k answer trees most relevant to the given query . We also enhance the method by extending the structure of the inverted list and adopting a relevance lookup table which enables more accurate estimation of the relevance scores of candidate root nodes and efficient search of top k answer trees . Performance evaluation by experiments with real graph datasets shows that the proposed method can find more effective top k answers than the previous approaches and provides acceptable and scalable execution performance for various types of keyword queries on large graph databases . 


Summary: We define a new measure of relevance of a node in the graph to a keyword query. We propose an extended answer structure for a top k query over graph databases. We propose an inverted list index and search algorithm to find top k answer trees. We enhanced the basic method for more efficient and scalable processing the query. Experiments show that the proposed method can find effective top k answers efficiently.
Teams are ranked to show their authority over each other . Existing methods rank the cricket teams using an ad hoc points system entirely based on the winning and losing of matches and ignores number of runs or wickets from which a team wins . In this paper adoptions of h index and PageRank are proposed for ranking teams to overcome the weakness of existing methods . Each team is represented by a node in the graph with two teams creates a weighted directed edge between each other by playing a match and the losing team points to the winning team . The intuition is to get more points for a team winning from a stronger team than winning from a weaker team by considering the number of runs or wickets also in addition to just winning and losing matches . The results show that proposed ranking methods provide quite promising insights of one day and test team rankings . The effect of damping factor d is also studied on the performance of PageRank based methods on both ODI and test matches teams ranking and interesting trends are found . 


Summary: Proposal of graph and count weightage based ranking algorithms. Addition of parameters number of runs and wickets for graph weightage. Study of the affect of damping factor on different PageRank based ranking methods.
In this work we develop new journal classification methods based on the h index . The introduction of the h index for research evaluation has attracted much attention in the bibliometric study and research quality evaluation . The main purpose of using an h index is to compare the index for different research units to differentiate their research performance . However the h index is defined by only comparing citations counts of one s own publications it is doubtful that the h index alone should be used for reliable comparisons among different research units like researchers or journals . In this paper we propose a new global h index where the publications in the core are selected in comparison with all the publications of the units to be evaluated . Furthermore we introduce some variants of the Gh index to address the issue of discrimination power . We show that together with the original h index they can be used to evaluate and classify academic journals with some distinct advantages in particular that they can produce an automatic classification into a number of categories without arbitrary cut off points . We then carry out an empirical study for classification of operations research and management science journals using this index and compare it with other well known journal ranking results such as the Association of Business Schools Journal Quality Guide and the Committee of Professors in OR ranking lists . 


Summary: We argue that the h index has a deficiency in comparing journals for ranking purpose. We propose the global h index Gh index and its extensions to fix this problem. We apply the new indexes to produce an automatic journal classification without arbitrary cut off points. The new ranking results are compatible with other peer reviewed based ranking results.
This study addresses the impact of domain expertise on the performance and query strategies used by users while searching for information . Twenty four experts and 24 non experts had to search for psychology information from the Universalis website in order to perform six information problems of varying complexity two simple problems two more difficult problems and two impossible problems . The results showed that participants with prior knowledge in the domain performed better than non experts . This difference was stronger as the complexity of the problems increased . This study also showed that experts and non experts displayed different query strategies . Experts reformulated the impossible problems more often than non experts because they produced new queries with psychology related keywords . The participants rarely used thematic category tool and when they did so this did not enhance their performance . 


Summary: Three complexity levels of information problems are defined related to psychology domain. 40 students in psychology and in other domains performed information problems with an online encyclopedia. Students in psychology performed better than the others especially for complex problems. Students in psychologies used more relevant strategies than the others. These expertise related differences are stronger for the complex problems.
Products that are not recycled at the end of their life increasingly damage the environment . In a collection remanufacturing scheme these end of life products can generate new profits . Designed on the personal computers industry this study defines an analytical model used to explore the implications of recycling on the reverse supply chain from an efficiency perspective for all participants in the process . The cases considered for analysis are the two and three echelon supply chains where we first look at the decentralized reverse setting followed by the coordinated setting through implementation of revenue sharing contract . We define customer willingness to return obsolete units as a function of the discount offered by the retailer in exchange for recycling devices with a remanufacturing value . The results show that performance measures and total supply chain profits improve through coordination with revenue sharing contracts on both two and three echelon reverse supply chains . 


Summary: Proposed an analytical model for the two and three echelon reverse supply chain. Defined the PC recycling supply chain as foundation for generating the analytical model. Analyzed the model behavior and profit implications by the applicability of a numerical example.
In contrast with their monolingual counterparts little attention has been paid to the effects that misspelled queries have on the performance of Cross Language Information Retrieval systems . The present work makes a first attempt to fill this gap by extending our previous work on monolingual retrieval in order to study the impact that the progressive addition of misspellings to input queries has this time on the output of CLIR systems . Two approaches for dealing with this problem are analyzed in this paper . Firstly the use of automatic spelling correction techniques for which in turn we consider two algorithms the first one for the correction of isolated words and the second one for a correction based on the linguistic context of the misspelled word . The second approach to be studied is the use of character n grams both as index terms and translation units seeking to take advantage of their inherent robustness and language independence . All these approaches have been tested on a from Spanish to English CLIR system that is Spanish queries on English documents . Real user generated spelling errors have been used under a methodology that allows us to study the effectiveness of the different approaches to be tested and their behavior when confronted with different error rates . The results obtained show the great sensitiveness of classic word based approaches to misspelled queries although spelling correction techniques can mitigate such negative effects . On the other hand the use of character n grams provides great robustness against misspellings . 


Summary: We study the effects of misspelled queries on the performance of CLIR systems. Word based approaches as both indexing and translation units are highly sensitive to the presence of misspellings. The use of correction mechanisms can significantly reduce their negative effects. Classical techniques are suitable for shorter queries while context based corrections are suitable for longer queries. Our approach based on character n grams as both indexing and translation units shows remarkable strength.
This paper examines the research patterns and trends of Recommendation System in China during the period of 2004 2013 . Data was collected from the China Academic Journal Network Publishing Database and the China Science Periodical Database . A co word analysis was conducted to measure correlation among the extracted keywords . The cluster analysis and social network analysis revealed 12 theme clusters network characteristics of the clusters the strategic diagram and the correlation network . The study results show that there are several important themes with a high correlation in Chinese RecSys research which is considered to be relatively focused mature and well developed overall . Some research themes have developed on a considerable scale while others remain isolated and undeveloped . This study also identified a few emerging themes with great potential for development . It was also determined that studies overall on the applications of RecSys are increasing . 


Summary: We examine RecSys studies in China quantitatively empirically and longitudinally. Research on RecSys is relatively mature and well developed overall. Twelve theme clusters and six larger branches are identified. Some undeveloped or immature research themes continue to persist. Emerging themes with great potential for development are also identified.
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task . New learning to rank methods are generally evaluated on benchmark test collections . However comparison of learning to rank methods based on evaluation results is hindered by the absence of a standard set of evaluation benchmark collections . In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets . Our comparison methodology consists of two components Normalized Winning Number which gives insight in the ranking accuracy of the learning to rank method and Ideal Winning Number which gives insight in the degree of certainty concerning its ranking accuracy . Evaluation results of 87 learning to rank methods on 20 well known benchmark datasets are collected through a structured literature search . ListNet SmoothRank FenchelRank FSMRank LRUF and LARF are Pareto optimal learning to rank methods in the Normalized Winning Number and Ideal Winning Number dimensions listed in increasing order of Normalized Winning Number and decreasing order of Ideal Winning Number . 


Summary: We propose a novel way to compare learning to rank methods. We perform a meta analysis on a large set of papers that report ranking accuracy on a benchmark dataset. LRUF FSMRank FenchelRank SmoothRank and ListNet are the most accurate with increasing certainty.
The worldwide propagation of mobile phone and the rapid development of location technologies have provided the chance to monitor freeway traffic conditions without requiring extra infrastructure investment . Over the past decade a number of research studies and operational tests have attempted to investigate the methods to estimate traffic measures using information from mobile phone . However most of these works ignored the fact that each vehicle has more than one phone due to the rapid popularity of mobile phone . This paper considered the circumstance of multi phones and proposed a relatively simplistic clustering technique to identify whether phones travel in the same vehicle . By using this technique mobile phone data can be used to determine not only speed but also vehicle counts by type and therefore density . A complex simulation covering different traffic condition and location accuracy of mobile phone has been developed to evaluate the proposed approach . Simulation results indicate that location accuracy of mobile phone is a crucial factor to estimate accurate traffic measures in case of a given location frequency and the number of continuous location data . In addition traffic demand and clustering method have a certain effect on the accuracy of traffic measures . 


Summary: We considered the fact that multi phones exist in a traveling vehicle. We used a clustering method to estimate freeway traffic measures from mobile phone location data. Three factors influence estimation accuracy of traffic measures. Further development of location technologies will improve estimation accuracy.
This paper presents a new relaxation technique to globally optimize mixed integer polynomial programming problems that arise in many engineering and management contexts . Using a bilinear term as the basic building block the underlying idea involves the discretization of one of the variables up to a chosen accuracy level . Multiparametric disaggregation technique for global optimization of polynomial programming problems . J. Glob . Optim . 55 227 251 by means of a radix based numeric representation system coupled with a residual variable to effectively make its domain continuous . Binary variables are added to the formulation to choose the appropriate digit for each position together with new sets of continuous variables and constraints leading to the transformation of the original mixed integer non linear problem into a larger one of the mixed integer linear programming type . The new underestimation approach can be made as tight as desired and is shown capable of providing considerably better lower bounds than a widely used global optimization solver for a specific class of design problems involving bilinear terms . 


Summary: New approach for transforming polynomial MINLP into MILP. Approximating upper bounding and relaxation lower bounding formulations are given. Bilinear term is basic building block where one variable is discretized. Base 2 to base 10 numeric representation systems can be employed. Problem size increases logarithmically with number of discrete points optimality gap decreases linearly.
We analyze the transitions from external search searching on web search engines to internal search searching on websites . We categorize 295 571 search episodes composed of a query submitted to web search engines and the subsequent queries submitted to a single website search by the same users . There are a total of 1 136 390 queries from all searches of which 295 571 are external search queries and 840 819 are internal search queries . We algorithmically classify queries into states and then use n grams to categorize search patterns . We cluster the searching episodes into major patterns and identify the most commonly occurring which are Explorers with a broad external search query and then broad internal search queries Navigators with an external search query containing a URL component and then specific internal search queries and Shifters with a different seemingly unrelated query types when transitioning from external to internal search . The implications of this research are that external search and internal search sessions are part of a single search episode and that online businesses can leverage these search episodes to more effectively target potential customers . 


Summary: External search and internal search cohere searching sessions. The referral query from external search can be predictive of internal searching. There are common patterns that one can use to classify internal searchers.
The Question Answering task aims to provide precise and quick answers to user questions from a collection of documents or a database . This kind of IR system is sorely needed with the dramatic growth of digital information . In this paper we address the problem of QA in the medical domain where several specific conditions are met . We propose a semantic approach to QA based on Natural Language Processing techniques which allow a deep analysis of medical questions and documents and semantic Web technologies at both representation and interrogation levels . We present our Semantic Question Answering System called MEANS and our proposed method for Answer Search based on semantic search and query relaxation . We evaluate the overall system performance on real questions and answers extracted from MEDLINE articles . Our experiments show promising results and suggest that a query relaxation strategy can further improve the overall performance . 


Summary: We propose a semantic question answering system MEANS for the medical domain. We introduce a novel query relaxation approach for question answering. MEANS integrates NLP methods allowing a deep analysis of questions and documents. MEANS uses semantic Web technologies and standards for data sharing and integration. Our experiments show promising results in terms of MRR and precision.