waashk/dblp huggingface dataset:
web augmentation of language models for continuous speech recognition of sms text messages. in this paper, we present an efficient query selection algorithm for the retrieval of web text data to augment a statistical language model (lm). the number of retrieved relevant documents is optimized with respect to the number of queries submitted. the querying scheme is applied in the domain of sms text messages. continuous speech recognition experiments are conducted on three languages: english, spanish, and french. the web data is utilized for augmenting in-domain lms in general and for adapting the lms to a user-specific vocabulary. word error rate reductions of up to 6.6% (in lm augmentation) and 26.0% (in lm adaptation) are obtained in setups, where the size of the web mixture lm is limited to the size of the baseline in-domain lm.
deriving generalized knowledge from corpora using wordnet abstraction. existing work in the extraction of commonsense knowledge from text has been primarily restricted to factoids that serve as statements about what may possibly obtain in the world. we present an approach to deriving stronger, more general claims by abstracting over large sets of factoids. our goal is to coalesce the observed nominals for a given predicate argument into a few predominant types, obtained as wordnet synsets. the results can be construed as generically quantified sentences restricting the semantic type of an argument position of a predicate.
unsupervised recognition of literal and non-literal use of idiomatic expressions. we propose an unsupervised method for distinguishing literal and non-literal usages of idiomatic expressions. our method determines how well a literal interpretation is linked to the overall cohesive structure of the discourse. if strong links can be found, the expression is classified as literal, otherwise as idiomatic. we show that this method can help to tell apart literal and non-literal usages, even for idioms which occur in canonical form.
human evaluation of a german surface realisation ranker. in this paper we present a human-based evaluation of surface realisation alternatives. we examine the relative rankings of naturally occurring corpus sentences and automatically generated strings chosen by statistical models (language model, log-linear model), as well as the naturalness of the strings chosen by the log-linear model. we also investigate to what extent preceding context has an effect on choice. we show that native speakers do accept quite some variation in word order, but there are also clearly factors that make certain realisation alternatives more natural.
predicting the fluency of text with shallow structural features: case studies of machine translation and human-written text. sentence fluency is an important component of overall text readability but few studies in natural language processing have sought to understand the factors that define it. we report the results of an initial study into the predictive power of surface syntactic statistics for the task; we use fluency assessments done for the purpose of evaluating machine translation. we find that these features are weakly but significantly correlated with fluency. machine and human translations can be distinguished with accuracy over 80%. the performance of pairwise comparison of fluency is also very high---over 90% for a multi-layer perceptron classifier. we also test the hypothesis that the learned models capture general fluency properties applicable to human-written text. the results do not support this hypothesis: prediction accuracy on the new data is only 57%. this finding suggests that developing a dedicated, task-independent corpus of fluency judgments will be beneficial for further investigations of the problem.
re-ranking models for spoken language understanding. spoken language understanding aims at mapping a natural language spoken sentence into a semantic representation. in the last decade two main approaches have been pursued: generative and discriminative models. the former is more robust to overfitting whereas the latter is more robust to many irrelevant features. additionally, the way in which these approaches encode prior knowledge is very different and their relative performance changes based on the task. in this paper we describe a machine learning framework where both models are used: a generative model produces a list of ranked hypotheses whereas a discriminative model based on structure kernels and support vector machines, re-ranks such list. we tested our approach on the media corpus (human-machine dialogs) and on a new corpus (human-machine and human-human dialogs) produced in the european luna project. the results show a large improvement on the state-of-the-art in concept segmentation and labeling.
on the use of comparable corpora to improve smt performance. we present a simple and effective method for extracting parallel sentences from comparable corpora. we employ a statistical machine translation (smt) system built from small amounts of parallel texts to translate the source side of the non-parallel corpus. the target side texts are used, along with other corpora, in the language model of this smt system. we then use information retrieval techniques and simple filters to create french/english parallel data from a comparable news corpora. we evaluate the quality of the extracted data by showing that it significantly improves the performance of an smt systems.
translation as weighted deduction. we present a unified view of many translation algorithms that synthesizes work on deductive parsing, semiring parsing, and efficient approximate search algorithms. this gives rise to clean analyses and compact descriptions that can serve as the basis for modular implementations. we illustrate this with several examples, showing how to build search spaces for several disparate phrase-based search strategies, integrate non-local features, and devise novel models. although the framework is drawn from parsing and applied to translation, it is applicable to many dynamic programming problems arising in natural language processing and other areas.
predicting strong associations on the basis of corpus data. current approaches to the prediction of associations rely on just one type of information, generally taking the form of either word space models or collocation measures. at the moment, it is an open question how these approaches compare to one another. in this paper, we will investigate the performance of these two types of models and that of a new approach based on compounding. the best single predictor is the log-likelihood ratio, followed closely by the document-based word space model. we will show, however, that an ensemble method that combines these two best approaches with the compounding algorithm achieves an increase in performance of almost 30% over the current state of the art.
person identification from text and speech genre samples. in this paper, we describe experiments conducted on identifying a person using a novel unique correlated corpus of text and audio samples of the person's communication in six genres. the text samples include essays, emails, blogs, and chat. audio samples were collected from individual interviews and group discussions and then transcribed to text. for each genre, samples were collected for six topics. we show that we can identify the communicant with an accuracy of 71% for six fold cross validation using an average of 22,000 words per individual across the six genres. for person identification in a particular genre (train on five genres, test on one), an average accuracy of 82% is achieved. for identification from topics (train on five topics, test on one), an average accuracy of 94% is achieved. we also report results on identifying a person's communication in a genre using text genres only as well as audio genres only.
outclassing wikipedia in open-domain information extraction: weakly-supervised acquisition of attributes over conceptual hierarchies. a set of labeled classes of instances is extracted from text and linked into an existing conceptual hierarchy. besides a significant increase in the coverage of the class labels assigned to individual instances, the resulting resource of labeled classes is more effective than similar data derived from the manually-created wikipedia, in the task of attribute extraction over conceptual hierarchies.
effects of word confusion networks on voice search. mobile voice-enabled search is emerging as one of the most popular applications abetted by the exponential growth in the number of mobile devices. the automatic speech recognition (asr) output of the voice query is parsed into several fields. search is then performed on a text corpus or a database. in order to improve the robustness of the query parser to noise in the asr output, in this paper, we investigate two different methods to query parsing. both methods exploit multiple hypotheses from asr, in the form of word confusion networks, in order to achieve tighter coupling between asr and query parsing and improved accuracy of the query parser. we also investigate the results of this improvement on search accuracy. word confusion-network based query parsing outperforms asr 1-best based query-parsing by 2.7% absolute and the search performance improves by 1.8% absolute on one of our data sets.
frequency matters: pitch accents and information status. this paper presents the results of a series of experiments which examine the impact of two information status categories (given and new) and frequency of occurrence on pitch accent realisations. more specifically the experiments explore within-type similarity of pitch accent productions and the effect information status and frequency of occurrence have on these productions. the results indicate a significant influence of both pitch accent type and information status category on the degree of within-type variability, in line with exemplartheoretic expectations.
correcting dependency annotation errors. building on work detecting errors in dependency annotation, we set out to correct local dependency errors. to do this, we outline the properties of annotation errors that make the task challenging and their existence problematic for learning. for the task, we define a feature-based model that explicitly accounts for non-relations between words, and then use ambiguities from one model to constrain a second, more relaxed model. in this way, we are successfully able to correct many errors, in a way which is potentially applicable to dependency parsing more generally.
parsing mildly non-projective dependency structures. we present parsing algorithms for various mildly non-projective dependency formalisms. in particular, algorithms are presented for: all well-nested structures of gap degree at most 1, with the same complexity as the best existing parsers for constituency formalisms of equivalent generative power; all well-nested structures with gap degree bounded by any constant k; and a new class of structures with gap degree up to k that includes some ill-nested structures. the third case includes all the gap degree k structures in a number of dependency treebanks.
text-to-text semantic similarity for automatic short answer grading. in this paper, we explore unsupervised techniques for the task of automatic short answer grading. we compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.
cognitively motivated features for readability assessment. we investigate linguistic features that correlate with the readability of texts for adults with intellectual disabilities (id). based on a corpus of texts (including some experimentally measured for comprehension by adults with id), we analyze the significance of novel discourse-level features related to the cognitive factors underlying our users' literacy challenges. we develop and evaluate a tool for automatically rating the readability of texts for these users. our experiments show that our discourse-level, cognitively-motivated features improve automatic readability assessment.
inference rules and their application to recognizing textual entailment. in this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. for this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application. the evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible improvements.
language-independent bilingual terminology extraction from a multilingual parallel corpus. we present a language-pair independent terminology extraction module that is based on a sub-sentential alignment system that links linguistically motivated phrases in parallel texts. statistical filters are applied on the bilingual list of candidate terms that is extracted from the alignment output. we compare the performance of both the alignment and terminology extraction module for three different language pairs (french-english, french-italian and french-dutch) and highlight language-pair specific problems (e.g. different compounding strategy in french and dutch). comparisons with standard terminology extraction programs show an improvement of up to 20% for bilingual terminology extraction and competitive results (85% to 90% accuracy) for monolingual terminology extraction, and reveal that the linguistically based alignment module is particularly well suited for the extraction of complex multiword terms.
mint: a method for effective and scalable mining of named entity transliterations from large comparable corpora. in this paper, we address the problem of mining transliterations of named entities (nes) from large comparable corpora. we leverage the empirical fact that multilingual news articles with similar news content are rich in named entity transliteration equivalents (netes). our mining algorithm, mint, uses a cross-language document similarity model to align multilingual news articles and then mines netes from the aligned articles using a transliteration similarity model. we show that our approach is highly effective on 6 different comparable corpora between english and 4 languages from 3 different language families. furthermore, it performs substantially better than a state-of-the-art competitor.
measuring frame relatedness. in this paper we introduce the notion of "frame relatedness", i.e. relatedness among prototypical situations as represented in the framenet database. we first demonstrate the cognitive plausibility of that notion through an annotation experiment, and then propose different types of computational measures to automatically assess relatedness. results show that our measures provide good performance on the task of ranking pairs of frames.
using lexical and relational similarity to classify semantic relations. many methods are available for computing semantic similarity between individual words, but certain nlp tasks require the comparison of word pairs. this paper presents a kernel-based framework for application to relational reasoning tasks of this kind. the model presented here combines information about two distinct types of word pair similarity: lexical similarity and relational similarity. we present an efficient and flexible technique for implementing relational similarity and show the effectiveness of combining lexical and relational models by demonstrating state-of-the-art results on a compound noun interpretation task.
lightly supervised transliteration for machine translation. we present a hebrew to english transliteration method in the context of a machine translation system. our method uses machine learning to determine which terms are to be transliterated rather than translated. the training corpus for this purpose includes only positive examples, acquired semi-automatically. our classifier reduces more than 38% of the errors made by a baseline method. the identified terms are then transliterated. we present an smt-based transliteration model trained with a parallel corpus extracted from wikipedia using a fairly simple method which requires minimal knowledge. the correct result is produced in more than 76% of the cases, and in 92% of the instances it is one of the top-5 results. we also demonstrate a small improvement in the performance of a hebrew-to-english mt system that uses our transliteration module.
performance confidence estimation for automatic summarization. we address the task of automatically predicting if summarization system performance will be good or bad based on features derived directly from either single- or multi-document inputs. our labelled corpus for the task is composed of data from large scale evaluations completed over the span of several years. the variation of data between years allows for a comprehensive analysis of the robustness of features, but poses a challenge for building a combined corpus which can be used for training and testing. still, we find that the problem can be mitigated by appropriately normalizing for differences within each year. we examine different formulations of the classification task which considerably influence performance. the best results are 84% prediction accuracy for single- and 74% for multi-document summarization.
an empirical study on class-based word sense disambiguation. as empirically demonstrated by the last senseval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. one possible reason could be the use of inappropriate set of meanings. in fact, wordnet has been used as a de-facto standard repository of meanings. however, to our knowledge, the meanings represented by wordnet have been only used for wsd at a very fine-grained sense level or at a very coarse-grained class level. we suspect that selecting the appropriate level of abstraction could be on between both levels. we use a very simple method for deriving a small set of appropriate meanings using basic structural properties of wordnet. we also empirically demonstrate that this automatically derived set of meanings groups senses into an adequate level of abstraction in order to perform class-based word sense disambiguation, allowing accuracy figures over 80%.
discovering global patterns in linguistic networks through spectral analysis: a case study of the consonant inventories. recent research has shown that language and the socio-cognitive phenomena associated with it can be aptly modeled and visualized through networks of linguistic entities. however, most of the existing works on linguistic networks focus only on the local properties of the networks. this study is an attempt to analyze the structure of languages via a purely structural technique, namely spectral analysis, which is ideally suited for discovering the global correlations in a network. application of this technique to phonet, the co-occurrence network of consonants, not only reveals several natural linguistic principles governing the structure of the consonant inventories, but is also able to quantify their relative importance. we believe that this powerful technique can be successfully applied, in general, to study the structure of natural languages.
clique-based clustering for improving named entity recognition systems. we propose a system which builds, in a semi-supervised manner, a resource that aims at helping a ner system to annotate corpus-specific named entities. this system is based on a distributional approach which uses syntactic dependencies for measuring similarities between named entities. the specificity of the presented method however, is to combine a clique-based approach and a clustering technique that amounts to a soft clustering method. our experiments show that the resource constructed by using this clique-based clustering system allows to improve different ner systems.
nlp and the humanities: the revival of an old liaison. this paper present an overview of some emerging trends in the application of nlp in the domain of the so-called digital humanities and discusses the role and nature of metadata, the annotation layer that is so characteristic of documents that play a role in the scholarly practises of the humanities. it is explained how metadata are the key to the added value of techniques such as text and link mining, and an outline is given of what measures could be taken to increase the chances for a bright future for the old ties between nlp and the humanities. there is no data like metadata!
tagging urdu text with parts of speech: a tagger comparison. in this paper, four state-of-art probabilistic taggers i.e. tnt tagger, treetagger, rf tagger and svm tool, are applied to the urdu language. for the purpose of the experiment, a syntactic tagset is proposed. a training corpus of 100,000 tokens is used to train the models. using the lexicon extracted from the training corpus, svm tool shows the best accuracy of 94.15%. after providing a separate lexicon of 70,568 types, svm tool again shows the best accuracy of 95.66%.
syntactic and semantic kernels for short text pair categorization. automatic detection of general relations between short texts is a complex task that cannot be carried out only relying on language models and bag-of-words. therefore, learning methods to exploit syntax and semantics are required. in this paper, we present a new kernel for the representation of shallow semantic information along with a comprehensive study on kernel methods for the exploitation of syntactic/semantic structures for short text pair categorization. our experiments with support vector machines on question/answer classification show that our kernels can be used to greatly improve system accuracy.
incremental parsing models for dialog task structure. in this paper, we present an integrated model of the two central tasks of dialog management: interpreting user actions and generating system actions. we model the interpretation task as a classification problem and the generation task as a prediction problem. these two tasks are interleaved in an incremental parsing-based dialog model. we compare three alternative parsing methods for this dialog model using a corpus of human-human spoken dialog from a catalog ordering domain that has been annotated for dialog acts and task/subtask information. we contrast the amount of context provided by each method and its impact on performance.
threading fundamental matrices. we present a new function that operates on fundamental matrices across a sequence of views. the operation, we call ¿threading¿, connects two consecutive fundamental matrices using the trifocal tensor as the connecting thread. the threading operation guarantees that consecutive camera matrices are consistent with a unique 3d model, without ever recovering a 3d model. applications include recovery of camera ego-motion from a sequence of views, image stabilization (plane stabilization) across a sequence, and multiview image-based rendering.