waashk/dblp huggingface dataset:
measuring the relative compositionality of verb-noun (v-n) collocations by integrating features. measuring the relative compositionality of multi-word expressions (mwes) is crucial to natural language processing. various collocation based measures have been proposed to compute the relative compositionality of mwes. in this paper, we define novel measures (both collocation based and context based measures) to measure the relative compositionality of mwes of v-n type. we show that the correlation of these features with the human ranking is much superior to the correlation of the traditional features with the human ranking. we then integrate the proposed features and the traditional features using a svm based ranking function to rank the collocations of v-n type based on their relative compositionality. we then show that the correlation between the ranks computed by the svm based ranking function and human ranking is significantly better than the correlation between ranking of individual features and human ranking.
identifying semantic relations and functional properties of human verb associations. this paper uses human verb associations as the basis for an investigation of verb properties, focusing on semantic verb relations and prominent nominal features. first, the lexical semantic taxonymy germanet is checked on the types of classic semantic relations in our data; verb-verb pairs not covered by germanet can help to detect missing links in the taxonomy, and provide a useful basis for defining non-classical relations. second, a statistical grammar is used for determining the conceptual roles of the noun responses. we present prominent syntax-semantic roles and evidence for the usefulness of co-occurrence information in distributional verb descriptions.
extracting information about outbreaks of infectious epidemics. this work demonstrates the promed-plus epidemiological fact base. the facts are automatically extracted from plain-text reports about outbreaks of infectious epidemics around the world. the system collects new reports, extracts new facts, and updates the database, in real time. the extracted database is available on-line through a web server.
paradigmatic modifiability statistics for the extraction of complex multi-word terms. we here propose a new method which sets apart domain-specific terminology from common non-specific noun phrases. it is based on the observation that terminological multi-word groups reveal a considerably lesser degree of distributional variation than non-specific noun phrases. we define a measure for the observable amount of paradigmatic modifiability of terms and, subsequently, test it on bigram, trigram and quadgram noun phrases extracted from a 104-million-word biomedical text corpus. using a community-wide curated biomedical terminology system as an evaluation gold standard, we show that our algorithm significantly outperforms a variety of standard term identification measures. we also provide empirical evidence that our methodolgy is essentially domain- and corpus-size-independent.
bootstrapping without the boot. "bootstrapping" methods for learning require a small amount of supervision to seed the learning process. we show that it is sometimes possible to eliminate this last bit of supervision, by trying many candidate seeds and selecting the one with the most plausible outcome. we discuss such "strapping" methods in general, and exhibit a particular method for strapping word-sense classifiers for ambiguous words. our experiments on the canadian hansards show that our unsupervised technique is significantly more effective than picking seeds by hand (yarowsky, 1995), which in turn is known to rival supervised methods.
kernel-based approach for automatic evaluation of natural language generation technologies: application to automatic summarization. in order to promote the study of automatic summarization and translation, we need an accurate automatic evaluation method that is close to human evaluation. in this paper, we present an evaluation method that is based on convolution kernels that measure the similarities between texts considering their substructures. we conducted an experiment using automatic summarization evaluation data developed for text summarization challenge 3 (tsc-3). a comparison with conventional techniques shows that our method correlates more closely with human evaluations and is more robust.
a large-scale exploration of effective global features for a joint entity detection and tracking model. entity detection and tracking (edt) is the task of identifying textual mentions of real-world entities in documents, extending the named entity detection and coreference resolution task by considering mentions other than names (pronouns, definite descriptions, etc.). like ne tagging and coreference resolution, most solutions to the edt task separate out the mention detection aspect from the coreference aspect. by doing so, these solutions are limited to using only local features for learning. in contrast, by modeling both aspects of the edt task simultaneously, we are able to learn using highly complex, non-local features. we develop a new joint edt model and explore the utility of many features, demonstrating their effectiveness on this task.
a translation model for sentence retrieval. in this work we propose a translation model for monolingual sentence retrieval. we propose four methods for constructing a parallel corpus. of the four methods proposed, a lexicon learned from a bilingual arabic-english corpus aligned at the sentence level performs best, significantly improving results over the query likelihood baseline. further, we demonstrate that smoothing from the local context of the sentence improves retrieval over the query likelihood baseline.
mining context specific similarity relationships using the world wide web. we have studied how context specific web corpus can be automatically created and mined for discovering semantic similarity relationships between terms (words or phrases) from a given collection of documents (target collection). these relationships between terms can be used to adjust the standard vectors space representation so as to improve the accuracy of similarity computation between text documents in the target collection. our experiments with a standard test collection (reuters) have revealed the reduction of similarity errors by up to 50%, twice as much as the improvement by using other known techniques.
mindnet: an automatically-created lexical resource. we will demonstrate mindnet, a lexical resource built automatically by processing text. we will present two forms of mindnet: as a static lexical resource, and, as a toolkit which allows mindnets to be built from arbitrary text. we will also introduce a web-based interface to mindnet lexicons (mnex) that is intended to make the data contained within mindnets more accessible for exploration. both english and japanese mindnets will be shown and will be made available, through mnex, for research purposes.
searching the audio notebook: keyword search in recorded conversation. mit's audio notebook added great value to the note-taking process by retaining audio recordings, e.g. during lectures or interviews. the key was to provide users ways to quickly and easily access portions of interest in a recording. several non-speech-recognition based techniques were employed. in this paper we present a system to search directly the audio recordings by key phrases. we have identified the user requirements as accurate ranking of phrase matches, domain independence, and reasonable response time. we address these requirements by a hybrid word/phoneme search in lattices, and a supporting indexing scheme. we will introduce the ranking criterion, a unified hybrid posterior-lattice representation, and the indexing algorithm for hybrid lattices. we present results for five different recording sets, including meetings, telephone conversations, and interviews. our results show an average search accuracy of 84%, which is dramatically better than a direct search in speech recognition transcripts (less than 40% search accuracy).
matching inconsistently spelled names in automatic speech recognizer output for information retrieval. many proper names are spelled inconsistently in speech recognizer output, posing a problem for applications where locating mentions of named entities is critical. we model the distortion in the spelling of a name due to the speech recognizer as the effect of a noisy channel. the models follow the framework of the ibm translation models. the model is trained using a parallel text of closed caption and automatic speech recognition output. we also test a string edit distance based method. the effectiveness of these models is evaluated on a name query retrieval task. our methods result in a 60% improvement in f1. we also demonstrate why the problem has not been critical in trec and tdt tasks.
maximum expected f-measure training of logistic regression models. we consider the problem of training logistic regression models for binary classification in information extraction and information retrieval tasks. fitting probabilistic models for use with such tasks should take into account the demands of the task-specific utility function, in this case the well-known f-measure, which combines recall and precision into a global measure of utility. we develop a training procedure based on empirical risk minimization / utility maximization and evaluate it on a simple extraction task.
using semantic relations to refine coreference decisions. we present a novel mechanism for improving reference resolution by using the output of a relation tagger to rescore coreference hypotheses. experiments show that this new framework can improve performance on two quite different languages - english and chinese.
integrating linguistic knowledge in passage retrieval for question answering. in this paper we investigate the use of linguistic knowledge in passage retrieval as part of an open-domain question answering system. we use annotation produced by a deep syntactic dependency parser for dutch, alpino, to extract various kinds of linguistic features and syntactic units to be included in a multi-layer index. similar annotation is produced for natural language questions to be answered by the system. from this we extract query terms to be sent to the enriched retrieval index. we use a genetic algorithm to optimize the selection of features and syntactic units to be included in a query. this algorithm is also used to optimize further parameters such as keyword weights. the system is trained on questions from the competition on dutch question answering within the cross-language evaluation forum (clef). we could show an improvement of about 15% in mean total reciprocal rank compared to traditional information retrieval using plain text keywords (including stemming and stop word removal).
pp-attachment disambiguation using large context. prepositional phrase-attachment is a common source of ambiguity in natural language. the previous approaches use limited information to solve the ambiguity -- four lexical heads -- although humans disambiguate much better when the full sentence is available. we propose to solve the pp-attachment ambiguity with a support vector machines learning model that uses complex syntactic and semantic features as well as unsupervised information obtained from the world wide web. the system was tested on several datasets obtaining an accuracy of 93.62% on a penn treebank-ii dataset; 91.79% on a framenet dataset when no manually-annotated semantic information is provided and 92.85% when semantic information is provided.
improving lsa-based summarization with anaphora resolution. we propose an approach to summarization exploiting both lexical information and the output of an automatic anaphoric resolver, and using singular value decomposition (svd) to identify the main terms. we demonstrate that adding anaphoric information results in significant performance improvements over a previously developed system, in which only lexical terms are used as the input to svd. however, we also show that how anaphoric information is used is crucial: whereas using this information to add new terms does result in improved performance, simple substitution makes the performance worse.
nooj: a linguistic annotation system for corpus processing. nooj is a new corpus processing system, similar to the intex software,1 and designed to replace it. nooj allows users to process large sets of texts in real time. users can build, accumulate and manage sophisticated concordances that correspond to morphological and syntactic grammars organized in re-usable libraries.
minimum sample risk methods for language modeling. this paper proposes a new discriminative training method, called minimum sample risk (msr), of estimating parameters of language models for text input. while most existing discriminative training methods use a loss function that can be optimized easily but approaches only approximately to the objective of minimum error rate, msr minimizes the training error directly using a heuristic training procedure. evaluations on the task of japanese text input show that msr can handle a large number of features and training samples; it significantly outperforms a regular trigram model trained using maximum likelihood estimation, and it also outperforms the two widely applied discriminative methods, the boosting and the perceptron algorithms, by a small but statistically significant margin.
disambiguation of morphological structure using a pcfg. german has a productive morphology and allows the creation of complex words which are often highly ambiguous. this paper reports on the development of a head-lexicalized pcfg for the disambiguation of german morphological analyses. the grammar is trained on unlabeled data using the inside-outside algorithm. the parser achieves a precision of more than 68% on difficult test data, which is 23% more than the baseline obtained by randomly choosing one of the simplest analyses. remarkable is the fact that precision drops to 52% without lexicalization.
making computers laugh: investigations in automatic humor recognition. humor is one of the most interesting and puzzling aspects of human behavior. despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. in this paper, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.
an orthonormal basis for topic segmentation in tutorial dialogue. this paper explores the segmentation of tutorial dialogue into cohesive topics. a latent semantic space was created using conversations from human to human tutoring transcripts, allowing cohesion between utterances to be measured using vector similarity. previous cohesion-based segmentation methods that focus on expository monologue are reapplied to these dialogues to create benchmarks for performance. a novel moving window technique using orthonormal bases of semantic vectors significantly outperforms these benchmarks on this dialogue segmentation task.
ocr post-processing for low density languages. we present a lexicon-free post-processing method for optical character recognition (ocr), implemented using weighted finite state machines. we evaluate the technique in a number of scenarios relevant for natural language processing, including creation of new ocr capabilities for low density languages, improvement of ocr performance for a native commercial system, acquisition of knowledge from a foreign-language dictionary, creation of a parallel text, and machine translation from ocr output.
flexible text segmentation with structured multilabel classification. many language processing tasks can be reduced to breaking the text into segments with prescribed properties. such tasks include sentence splitting, tokenization, named-entity extraction, and chunking. we present a new model of text segmentation based on ideas from multilabel classification. using this model, we can naturally represent segmentation problems involving overlapping and non-contiguous segments. we evaluate the model on entity extraction and noun-phrase chunking and show that it is more accurate for overlapping and non-contiguous segments, but it still performs well on simpler data sets for which sequential tagging has been the best method.
differentiating homonymy and polysemy in information retrieval. recent studies into web retrieval have shown that word sense disambiguation can increase retrieval effectiveness. however, it remains unclear as to the minimum disambiguation accuracy required and the granularity with which one must define word sense in order to maximize these benefits. this study answers these questions using a simulation of the effects of ambiguity on information retrieval. it goes beyond previous studies by differentiating between homonymy and polysemy. results show that retrieval is more sensitive to polysemy than homonymy and that, when resolving polysemy, accuracy as low as 55% can potentially lead to increased performance.
multi-perspective question answering using the opqa corpus. we investigate techniques to support the answering of opinion-based questions. we first present the opqa corpus of opinion questions and answers. using the corpus, we compare and contrast the properties of fact and opinion questions and answers. based on the disparate characteristics of opinion vs. fact answers, we argue that traditional fact-based qa approaches may have difficulty in an mpqa setting without modification. as an initial step towards the development of mpqa systems, we investigate the use of machine learning and rule-based subjectivity and opinion source filters and show that they can be used to guide mpqa systems.
hidden-variable models for discriminative reranking. we describe a new method for the representation of nlp structures within reranking approaches. we make use of a conditional log-linear model, with hidden variables representing the assignment of lexical items to word clusters or word senses. the model learns to automatically make these assignments based on a discriminative training criterion. training and decoding with the model requires summing over an exponential number of hidden-variable assignments: the required summations can be computed efficiently and exactly using dynamic programming. as a case study, we apply the model to parse reranking. the model gives an f-measure improvement of &ap; 1.25% beyond the base parser, and an &ap; 0.25% improvement beyond the collins (2000) reranker. although our experiments are focused on parsing, the techniques described generalize naturally to nlp structures other than parse trees.
cross-linguistic projection of role-semantic information. this paper considers the problem of automatically inducing role-semantic annotations in the framenet paradigm for new languages. we introduce a general framework for semantic projection which exploits parallel texts, is relatively inexpensive and can potentially reduce the amount of effort involved in creating semantic resources. we propose projection models that exploit lexical and syntactic information. experimental results on an english-german parallel corpus demonstrate the advantages of this approach.
morphology and reranking for the statistical parsing of spanish. we present two methods for incorporating detailed features in a spanish parser, building on a baseline model that is a lexicalized pcfg. the first method exploits spanish morphology, and achieves an f1 constituency score of 83.6%. this is an improvement over 81.2% accuracy for the baseline, which makes little or no use of morphological information. the second model uses a reranking approach to add arbitrary global features of parse trees to the morphological model. the reranking model reaches 85.1% f1 accuracy on the spanish parsing task. the resulting model for spanish parsing combines an approach that specifically targets morphological information with an approach that makes use of general structural features.
classummary: introducing discussion summarization to online classrooms. this paper describes a novel summarization system, classummary, for interactive online classroom discussions. this system is originally designed for open source software (oss) development forums. however, this new application provides valuable feedback on designing summarization systems and applying them to everyday use, in addition to the traditional natural language processing evaluation methods. in our demonstration at hlt, new users will be able to direct this summarizer themselves.
learning a spelling error model from search query logs. applying the noisy channel model to search query spelling correction requires an error model and a language model. typically, the error model relies on a weighted string edit distance measure. the weights can be learned from pairs of misspelled words and their corrections. this paper investigates using the expectation maximization algorithm to learn edit distance weights directly from search query logs, without relying on a corpus of paired words.
learning mixed initiative dialog strategies by using reinforcement learning on both conversants. this paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. with this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. the specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. the results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.
emotions from text: machine learning for text-based emotion prediction. in addition to information, text contains attitudinal, and more specifically, emotional content. this paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the snow learning architecture. the goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a na&iuml;ve baseline and bow approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. we also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. in addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.
analyzing models for semantic role assignment using confusability. we analyze models for semantic role assignment by defining a meta-model that abstracts over features and learning paradigms. this meta-model is based on the concept of role confusability, is defined in information-theoretic terms, and predicts that roles realized by less specific grammatical functions are more difficult to assign. we find that confusability is strongly correlated with the performance of classifiers based on syntactic features, but not for classifiers including semantic features. this indicates that syntactic features approximate a description of grammatical functions, and that semantic features provide an independent second view on the data.