waashk/dblp huggingface dataset:
building emotion lexicon from weblog corpora. an emotion lexicon is an indispensable resource for emotion analysis. this paper aims to mine the relationships between words and emotions using weblog corpora. a collocation model is proposed to learn emotion lexicons from weblog articles. emotion classification at sentence level is experimented by using the mined lexicons to demonstrate their usefulness.
computing lexical chains with graph clustering. this paper describes a new method for computing lexical chains. these are sequences of semantically related words that reflect a text's cohesive structure. in contrast to previous methods, we are able to select chains based on their cohesive strength. this is achieved by analyzing the connectivity in graphs representing the lexical chains. we show that the generated chains significantly improve performance of automatic text summarization and keyphrase indexing.
identifying linguistic structure in a quantitative analysis of dialect pronunciation. the aim of this paper is to present a new method for identifying linguistic structure in the aggregate analysis of the language variation. the method consists of extracting the most frequent sound correspondences from the aligned transcriptions of words. based on the extracted correspondences every site is compared to all other sites, and a correspondence index is calculated for each site. this method enables us to identify sound alternations responsible for dialect divisions and to measure the extent to which each alternation is responsible for the divisions obtained by the aggregate analysis.
japanese dependency parsing using sequential labeling for semi-spoken language. the amount of documents directly published by end users is increasing along with the growth of web 2.0. such documents often contain spoken-style expressions, which are difficult to analyze using conventional parsers. this paper presents dependency parsing whose goal is to analyze japanese semi-spoken expressions. one characteristic of our method is that it can parse self-dependent (independent) segments using sequential labeling.
semantic classification of noun phrases using web counts and learning algorithms. this paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. the attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. we compare and evaluate different algorithms and different joining phrases on nastase and szpakowicz's (2003) dataset of 600 modifier-noun compounds. we find that by using a support vector machine classifier we can obtain better performance on this dataset than a current state-of-the-art system; even with a relatively small set of prepositional joining terms.
extractive summarization based on event term clustering. event-based summarization extracts and organizes summary sentences in terms of the events that the sentences describe. in this work, we focus on semantic relations among event terms. by connecting terms with relations, we build up event term graph, upon which relevant terms are grouped into clusters. we assume that each cluster represents a topic of documents. then two summarization strategies are investigated, i.e. selecting one term as the representative of each topic so as to cover all the topics, or selecting all terms in one most significant topic so as to highlight the relevant information related to this topic. the selected terms are then responsible to pick out the most appropriate sentences describing them. the evaluation of clustering-based summarization on duc 2001 document sets shows encouraging improvement over the well-known pagerank-based summarization.
nict-atr speech-to-speech translation system. this paper describes the latest version of speech-to-speech translation systems developed by the team of nict-atr for over twenty years. the system is now ready to be deployed for the travel domain. a new noise-suppression technique notably improves speech recognition performance. corpus-based approaches of recognition, translation, and synthesis enable coverage of a wide variety of topics and portability to other languages.
ensemble document clustering using weighted hypergraph generated by nmf. in this paper, we propose a new ensemble document clustering method. the novelty of our method is the use of non-negative matrix factorization (nmf) in the generation phase and a weighted hypergraph in the integration phase. in our experiment, we compared our method with some clustering methods. our method achieved the best results.
inducing combinatory categorial grammars with genetic algorithms. this paper proposes a novel approach to the induction of combinatory categorial grammars (ccgs) by their potential affinity with the genetic algorithms (gas). specifically, ccgs utilize a rich yet compact notation for lexical categories, which combine with relatively few grammatical rules, presumed universal. thus, the search for a ccg consists in large part in a search for the appropriate categories for the data-set's lexical items. we present and evaluates a system utilizing a simple ga to successively search and improve on such assignments. the fitness of categorial-assignments is approximated by the coverage of the resulting grammar on the data-set itself, and candidate solutions are updated via the standard ga techniques of reproduction, crossover and mutation.
adaptive string distance measures for bilingual dialect lexicon induction. this paper compares different measures of graphemic similarity applied to the task of bilingual lexicon induction between a swiss german dialect and standard german. the measures have been adapted to this particular language pair by training stochastic transducers with the expectation-maximisation algorithm or by using handmade transduction rules. these adaptive metrics show up to 11% f-measure improvement over a static metric like levenshtein distance.
logistic online learning methods and their application to incremental dependency parsing. we investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. the update rules are based on multinomial logistic models. the most interesting question for such an approach is how to integrate the cost function into the learning paradigm. we propose a number of solutions to this problem. to demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. these tasks were conventional multiclass classification, hiearchical classification, and a structured classification task: complete labeled dependency tree prediction. the performance figures of the logistic algorithms range from slightly lower to slightly higher than margin-based online algorithms.
using error-correcting output codes with model-refinement to boost centroid text classifier. in this work, we investigate the use of error-correcting output codes (ecoc) for boosting centroid text classifier. the implementation framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. however, this kind of decomposition incurs considerable bias for centroid classifier, which results in noticeable degradation of performance for centroid classifier. in order to address this issue, we use model-refinement to adjust this so-called bias. the basic idea is to take advantage of misclassified examples in the training data to iteratively refine and adjust the centroids of text data. the experimental results reveal that model-refinement can dramatically decrease the bias introduced by ecoc, and the combined classifier is comparable to or even better than svm classifier in performance.
mimus: a multimodal and multilingual dialogue system for the home domain. this paper describes mimus, a multimodal and multilingual dialogue system for the in--home scenario, which allows users to control some home devices by voice and/or clicks. its design relies on wizard of oz experiments and is targeted at disabled users. mimus follows the information state update approach to dialogue management, and supports english, german and spanish, with the possibility of changing language on--the--fly. mimus includes a gestures--enabled talking head which endows the system with a human--like personality.
test collection selection and gold standard generation for a multiply-annotated opinion corpus. opinion analysis is an important research topic in recent years. however, there are no common methods to create evaluation corpora. this paper introduces a method for developing opinion corpora involving multiple annotators. the characteristics of the created corpus are discussed, and the methodologies to select more consistent testing collections and their corresponding gold standards are proposed. under the gold standards, an opinion extraction system is evaluated. the experiment results show some interesting phenomena.
classifying temporal relations between events. this paper describes a fully automatic two-stage machine learning architecture that learns temporal relations between pairs of events. the first stage learns the temporal attributes of single event descriptions, such as tense, grammatical aspect, and aspectual class. these imperfect guesses, combined with other linguistic features, are then used in a second stage to classify the temporal relationship between two events. we present both an analysis of our new features and results on the timebank corpus that is 3% higher than previous work that used perfect human tagged features.
learning to rank definitions to generate quizzes for interactive information presentation. this paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate "who is this?" quizzes. the definitions are ordered according to how difficult they make it to name the person. such ranking would enable users to interactively learn about a person through dialogue with a system with improved understanding and lasting motivation, which is useful for educational systems. in our approach, we train a ranker that learns from data the appropriate ranking of definitions based on features that encode the importance of keywords in a definition as well as its content. experimental results show that our approach is significantly better in ranking definitions than baselines that use conventional information retrieval measures such as tf*idf and pointwise mutual information (pmi).
expanding indonesian-japanese small translation dictionary using a pivot language. we propose a novel method to expand a small existing translation dictionary to a large translation dictionary using a pivot language. our method depends on the assumption that it is possible to find a pivot language for a given language pair on condition that there are both a large translation dictionary from the source language to the pivot language, and a large translation dictionary from the pivot language to the destination language. experiments that expands the indonesian-japanese dictionary using the english language as a pivot language shows that the proposed method can improve performance of a real clir system.
minimally lexicalized dependency parsing. dependency structures do not have the information of phrase categories in phrase structure grammar. thus, dependency parsing relies heavily on the lexical information of words. this paper discusses our investigation into the effectiveness of lexicalization in dependency parsing. specifically, by restricting the degree of lexicalization in the training phase of a parser, we examine the change in the accuracy of dependency relations. experimental results indicate that minimal or low lexicalization is sufficient for parsing accuracy.
support vector machines for query-focused summarization trained and evaluated on pyramid data. this paper presents the use of support vector machines (svm) to detect relevant information to be included in a query-focused summary. several svms are trained using information from pyramids of summary content units. their performance is compared with the best performing systems in duc-2005, using both rouge and autopan, an automatic scoring method for pyramid evaluation.
a joint statistical model for simultaneous word spacing and spelling error correction for korean. this paper presents noisy-channel based korean preprocessor system, which corrects word spacing and typographical errors. the proposed algorithm corrects both errors simultaneously. using eojeol transition pattern dictionary and statistical data such as eumjeol n-gram and jaso transition probabilities, the algorithm minimizes the usage of huge word dictionaries.
disambiguating between generic and referential "you" in dialog. we describe an algorithm for a novel task: disambiguating the pronoun you in conversation. you can be generic or referential; finding referential you is important for tasks such as addressee identification or extracting 'owners' of action items. our classifier achieves 84% accuracy in two-person conversations; an initial study shows promising performance even on more complex multi-party meetings.
a feature based approach to leveraging context for classifying newsgroup style discussion segments. on a multi-dimensional text categorization task, we compare the effectiveness of a feature based approach with the use of a state-of-the-art sequential learning technique that has proven successful for tasks such as "email act classification". our evaluation demonstrates for the three separate dimensions of a well established annotation scheme that novel thread based features have a greater and more consistent impact on classification performance.
constraint reasoning in deep biomedical models. objective:: deep biomedical models are often expressed by means of differential equations. despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. the objective of this work is to propose a constraint reasoning framework to support safe decisions based on deep biomedical models. method:: the methods used in our approach include the generic constraint propagation techniques for reducing the bounds of uncertainty of the numerical variables complemented with new constraint reasoning techniques that we developed to handle differential equations. results:: the results of our approach are illustrated in biomedical models for the diagnosis of diabetes, tuning of drug design and epidemiology where it was a valuable decision-supporting tool notwithstanding the uncertainty on data. conclusion:: the main conclusion that follows from the results is that, in biomedical decision support, constraint reasoning may be a worthwhile alternative to traditional simulation methods, especially when safe decisions are required.
content collection for the labelling of health-related web content. as the number of health-related web sites in various languages increases, so does the need for control mechanisms that give the users adequate guarantee on whether the web resources they are visiting meet a minimum level of quality standards. based upon state-of-the-art technology in the areas of semantic web, content analysis and quality labelling, the medieq project, integrates existing technologies and tests them in a novel application: the automation of the labelling process in health-related web content. medieq provides tools that crawl the web to locate unlabelled health web resources, to label them according to pre-defined labelling criteria, as well as to monitor them. this paper focuses on content collection and discusses our experiments in the english language.
an application of machine learning in the diagnosis of ischaemic heart disease. ischaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. the four diagnostic levels consist of evaluation of signs and symptoms of the disease and ecg (electrocardiogram) at rest, sequential ecg testing during the controlled exercise, myocardial scintigraphy and finally coronary angiography. the diagnostic process is stepwise and the results are interpreted hierarchically, i.e, the next step is necessary only if the results of the former are inconclusive. because suggestibility is possible, the results of each step are interpreted individually and only the results of the highest step are valid. on the other hand, machine learning methods may be capable of objective interpretation of all available results for the same patient and in this way increase the diagnostic accuracy, sensitivity and specificity of each step. in the usual setting, the machine learning algorithms are tuned to maximize classification accuracy. in our case, the sensitivity and specificity were much more important, so we generalized the algorithms to take in account the variable misclassification costs. the costs can be tuned in order to bias the algorithms towards higher sensitivity or specificity. we conducted many experiments with four learning algorithms and different variations of our dataset (327 patients with completed diagnostic procedures). our results show that improvements using machine learning techniques are reasonable and might find good use in practice.
extending temporal databases to deal with telic/atelic medical data. objective: in this paper, we aim at defining a general-purpose data model and query language coping with both ''telic'' and ''atelic'' medical data. background: in the area of medical informatics, there is an increasing realization that temporal information plays a crucial role, so that suitable database models and query languages are needed to store and support it. however, despite the wide range of approaches in the area, in this paper we show that a relevant class of medical data cannot be properly dealt with. methodology: we first show that data models based on the ''point-based'' semantics, which is (implicitly or explicitly) assumed by the totality of temporal database approaches, have several limitations when dealing with ''telic'' data. we then propose a new model (based on the ''interval-based'' semantics) to cope with such data, and extend the query language accordingly. results: we propose a new three-sorted model and a query language to properly deal with both ''telic'' and ''atelic'' medical data (as well as non-temporal data). our query language is flexible, since it allows one to switch from ''atelic'' to ''telic'' data, and vice versa. conclusion: in this paper, we demostrate the feasibilty of a database approach copying with both telic and atelic data as needed in several (medical) applications.
credal classification for dementia screening. dementia is a very serious personal, medical and social problem. early and accurate diagnoses seem to be the key to effectively cope with it. this paper presents a diagnostic tool that couples the most widely used computerized system of cognitive tests in dementia research, the cognitive drug research system, with the naive credal classifier. although the classifier is trained on an incomplete database, it provides unmatched predictive performance and reliability. the tool also proves to be very effective in discriminating between alzheimer''s disease and dementia with lewy bodies, which is a problem on the frontier of research on dementia.
bayesian network decomposition for modeling breast cancer detection. the automated differentiation between benign and malignant abnormalities is a difficult problem in the breast cancer domain. while previous studies consider a single bayesian network approach, in this paper we propose a novel perspective based on bayesian network decomposition. we consider three methods that allow for different (levels of) network topological or structural decomposition. through examples, we demonstrate some advantages of bayesian network decomposition for the problem at hand: (i) natural and more intuitive representation of breast abnormalities and their features (ii) compact representation and efficient manipulation of large conditional probability tables, and (iii) a possible improvement in the knowledge acquisition and representation processes.
adaptive optimization of hospital resource calendars. as demand for health care increases, a high efficiency on limited resources is necessary for affordable high patient service levels. here, we present an adaptive approach to efficient resource usage by automatic optimization of resource calendars. we describe a precise model based on a case study at the radiology department of the academic medical center amsterdam (amc). we model the properties of the different groups of patients, with additional differentiating urgency levels. based on this model, we develop a detailed simulation that is able to replicate the known scheduling problems. in particular, the simulation shows that due to fluctuations in demand, the allocations in the resource calendar must be flexible in order to make efficient use of the resources. we develop adaptive algorithms to automate iterative adjustments to the resource calendar. to test the effectiveness of our approach, we evaluate the algorithms using the simulation. our adaptive optimization approach is able to maintain overall target performance levels while the resource is used at high efficiency.
inference in the promedas medical expert system. in the current paper, the promedas model for internal medicine, developed by our team, is introduced. the model is based on up-to-date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. we show that belief propagation (bp) can be successfully applied as approximate inference algorithm in the promedas network. in some cases, however, we find errors that are too large for this application. we apply a recently developed method that improves the bp results by means of a loop expansion scheme. this method, termed loop corrected (lc) bp, is able to improve the marginal probabilities significantly, leaving a remaining error which is acceptable for the purpose of medical diagnosis.
an experiment in automatic classification of pathological reports. medical reports are predominantly written in natural language; as such they are not computer-accessible. a common way to make medical narrative accessible to automated systems is by assigning `computer-understandable' keywords from a controlled vocabulary. experts usually perform this task by hand. in this paper, we investigate methods to support or automate this type of medical classification. we report on experiments using the palga data set, a collection of 14 million pathological reports, each of which has been classified by a domain expert. we describe methods for automatically categorizing the documents in this data set in an accurate way. in order to evaluate the proposed automatic classification approaches, we compare their output with that of two additional human annotators. while the automatic system performs well in comparison with humans, the inconsistencies within the annotated data constrain the maximum attainable performance.