waashk/dblp huggingface dataset:
class-based probability estimation using a semantic hierarchy. this paper concerns the acquisition of a particular kind of lexical knowledge, namely the knowledge of which noun senses can fill argument slots of predicates. probabilities are used to represent the knowledge, and classes from a semantic hierarchy are used to estimate the probabilities. there is a particular focus on the problem of how to determine a suitable class, or level of generalisation, in the hierarchy. a pseudo disambiguation task is used to compare different class-based estimation methods.
information-based machine translation. this paper describes an approach to machine translation that places linguistic information at its foundation. the difficulty of translation from english to japanese is illustrated with data that shows the influence of various linguistic contextual factors. next, a method for natural language transfer is presented that integrates translation examples (represented as typed feature structures with source-target indices) with linguistic rules and constraints. the method has been implemented, and the results of an evaluation are presented.
transformation based learning in the fast lane. transformation-based learning has been successfully employed to solve many natural language processing problems. it achieves state-of-the-art performance on many natural language processing tasks and does not overtrain easily. however, it does have a serious drawback: the training time is often intorelably long, especially on the large corpora which are often used in nlp. in this paper, we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacrificing performance. the paper compares and contrasts the training time needed and performance achieved by our modified learner with two other systems: a standard transformation-based learner, and the ica system (hepple, 2000). the results of these experiments show that our system is able to achieve a significant improvement in training time while still achieving the same performance as a standard transformation-based learner. this is a valuable contribution to systems and algorithms which utilize transformation-based learning at any part of the execution.
an algorithm for aspects of semantic interpretation using an enhanced wordnet. an algorithm for semantic interpretation is explained. the algorithm is based on predicates defined for wordnet verb classes. the algorithm is driven by the definition of these predicates whose thematic roles are linked to the wordnet ontology for nouns and to the syntactic relations that realize them. the algorithm has been tested in the identification of the meaning of the verb, thematic roles, and temporal and spatial adjuncts.
generating training data for medical dictations. in automatic speech recognition (asr) enabled applications for medical dictations, corpora of literal transcriptions of speech are critical for training both speaker independent and speaker adapted acoustic models. obtaining these transcriptions is both costly and time consuming. non-literal transcriptions, on the other hand, are easy to obtain because they are generated in the normal course of a medical transcription operation. this paper presents a method of automatically generating texts that can take the place of literal transcriptions for training acoustic and language models. atrs is an automatic transcription reconstruction system that can produce near-literal transcriptions with almost no human labor. we will show that (i) adapted acoustic models trained on atrs data perform as well as or better than adapted acoustic models trained on literal transcriptions (as measured by recognition accuracy) and (ii) language models trained on atrs data have lower perplexity than language models trained on non-literal data.
why inverse document frequency? inverse document frequency (idf) is a popular measure of a word's importance. the idf invariably appears in a host of heuristic measures used in information retrieval. however, so far the idf has itself been a heuristic. in this paper, we show idf to be optimal in a principled sense. we show that idf is the optimal weight of a word with respect to minimization of a kullback-leibler distance suitably generalized to nonnegative functions which need not be probability distributions. this optimization problem is closely related to maximum entropy problem. we show that the idf is the optimal weight associated with a word-feature in an information retrieval setting where we treat each document as the query that retrieves itself. that is, idf is optimal for document self-retrieval.
a decision tree of bigrams is an accurate predictor of word sense. this paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. this approach is evaluated using the sense-tagged corpora from the 1998 senseval word sense disambiguation exercise. it is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.
a probabilistic earley parser as a psycholinguistic model. in human sentence processing, cognitive load can be defined many ways. this report considers a definition of cognitive load in terms of the total probability of structural options that have been disconfirmed at some point in a sentence: the surprisal of word wi given its prefix wo...i-1 on a phrase-structural language model. these loads can be efficiently calculated using a probabilistic earley parser (stolcke, 1995) which is interpreted as generating predictions about reading time on a word-by-word basis. under grammatical assumptions supported by corpus-frequency data, the operation of stolcke's probabilistic earley parser correctly predicts processing phenomena associated with garden path structural ambiguity and with the subject/object relative asymmetry.
text and knowledge mining for coreference resolution. traditionally coreference is resolved by satisfying a combination of salience, syntactic, semantic and discourse constraints. the acquisition of such knowledge is time-consuming, difficult and error-prone. therefore, we present a knowledge minimalist methodology of mining coreference rules from annotated text corpora. semantic consistency evidence, which is a form of knowledge required by coreference, is easily retrieved from wordnet. additional consistency knowledge is discovered by a meta-bootstrapping algorithm applied to unlabeled texts.
re-engineering letter-to-sound rules. using finite-state automata for the text analysis component in a text-to-speech system is problematic in several respects: the rewrite rules from which the automata are compiled are difficult to write and maintain, and the resulting automata can become very large and therefore inefficient. converting the knowledge represented explicitly in rewrite rules into a more efficient format is difficult. we take an indirect route, learning an efficient decision tree representation from data and tapping information contained in existing rewrite rules, which increases performance compared to learning exclusively from a pronunciation lexicon.
do cfg-based language models need agreement constraints? many people are now routinely building grammar-based language models for interactive spoken language applications; these language models are typically ad hoc semantic grammars which ignore many standard linguistic constraints, in particular grammatical agreement. we describe a series of experiments in which we took three cfg-based language models from non-trivial implemented systems, and in each case contrasted the performance of a version which included agreement constraints against a version which ignored them. our findings suggest that inclusion of agreement constraints significantly improves performance in terms of both word error rate and semantic error rate.
applying co-training methods to statistical parsing. we propose a novel co-training method for statistical parsing. the algorithm takes as input a small corpus (9695 sentences) annotated with parse trees, a dictionary of possible lexicalized structures for each word in the training set and a large pool of unlabeled text. the algorithm iteratively labels the entire data set with parse trees. using empirical results based on parsing the wall street journal corpus we show that training a statistical parser on the combined labeled and unlabeled data strongly out-performs training only on the labeled data.
knowledge-free induction of inflectional morphologies. we propose an algorithm to automatically induce the morphology of inflectional languages using only text corpora and no human input. our algorithm combines cues from orthography, semantics, and syntactic distributions to induce morphological relationships in german, dutch, and english. using celex as a gold standard for evaluation, we show our algorithm to be an improvement over any knowledge-free algorithm yet proposed.
tree-cut and a lexicon based on systematic polysemy. this paper describes a lexicon organized around systematic polysemy: a set of word senses that are related in systematic and predictable ways. the lexicon is derived by a fully automatic extraction method which utilizes a clustering technique called tree-cut. we compare our lexicon to wordnet cousins, and the inter-annotator disagreement observed between wordnet semcor and dso corpora.
a structured language model based on context-sensitive probabilistic left-corner parsing. recent contributions to statistical language modeling for speech recognition have shown that probabilistically parsing a partial word sequence aids the prediction of the next word, leading to "structured" language models that have the potential to outperform n-grams. existing approaches to structured language modeling construct nodes in the partial parse tree after all of the underlying words have been predicted. this paper presents a different approach, based on probabilistic left-corner grammar (plcg) parsing, that extends a partial parse both from the bottom up and from the top down, leading to a more focused and more accurate, though somewhat less robust, search of the parse space. at the core of our new structured language model is a fast context-sensitive and lexicalized plcg parsing algorithm that uses dynamic programming. preliminary perplexity and word-accuracy results appear to be competitive with previous ones, while speed is increased.
instance-based natural language generation. this paper presents a bottom-up generator that makes use of information retrieval techniques to rank potential generation candidates by comparing them to a data base of stored instances. we introduce two general techniques to address the search problem, expectation-driven search and dynamic grammar rule selection, and present the architecture of an implemented generation system called igen. our approach uses a domain-specific generation grammar that is automatically derived from a semantically tagged treebank. we then evaluate the efficiency of our system.
spot: a trainable sentence planner. sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. in this paper, we present spot, a sentence planner, and a new methodology for automatically training spot on the basis of feedback provided by human judges. we reconceptualize the task into two distinct phases. first, a very simple, randomized sentence-plan-generator (spg) generates a potentially large list of possible sentence plans for a given text-plan input. second, the sentence-plan-ranker (spr) ranks the list of output sentence plans, and then selects the top-ranked plan. the spr uses ranking rules automatically learned from training data. we show that the trained spr learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.
inducing multilingual pos taggers and np bracketers via robust projection across aligned corpora. this paper investigates the potential for projecting linguistic annotations including part-of-speech tags and base noun phrase bracketings from one language to another via automatically word-aligned parallel corpora. first, experiments assess the accuracy of unmodified direct transfer of tags and brackets from the source language english to the target languages french and chinese, both for noisy machine-aligned sentences and for clean hand-aligned sentences. performance is then substantially boosted over both of these baselines by using training techniques optimized for very noisy data, yielding 94-96% core french part-of-speech tag accuracy and 90% french bracketing f-measure for stand-alone monolingual tools trained without the need for any human-annotated data in the given language.
a comparative study on language model adaptation techniques using new evaluation metrics. this paper presents comparative experimental results on four techniques of language model adaptation, including a maximum a posteriori (map) method and three discriminative training methods, the boosting algorithm, the average perceptron and the minimum sample risk method, on the task of japanese kana-kanji conversion. we evaluate these techniques beyond simply using the character error rate (cer): the cer results are interpreted using a metric of domain similarity between background and adaptation domains, and are further evaluated by correlating them with a novel metric for measuring the side effects of adapted models. using these metrics, we show that the discriminative methods are superior to a map-based method not only in terms of achieving larger cer reduction, but also of being more robust against the similarity of background and adaptation domains, and achieve larger cer reduction with fewer side effects.
using sketches to estimate associations. we should not have to look at the entire corpus (e.g., the web) to know if two words are associated or not. a powerful sampling technique called sketches was originally introduced to remove duplicate web pages. we generalize sketches to estimate contingency tables and associations, using a maximum likelihood estimator to find the most likely contingency table given the sample, the margins (document frequencies) and the size of the collection. not unsurprisingly, computational work and statistical accuracy (variance or errors) depend on sampling rate, as will be shown both theoretically and empirically. sampling methods become more and more important with larger and larger collections. at web scale, sampling rates as low as 10-4 may suffice.
a semi-supervised feature clustering algorithm with application to word sense disambiguation. in this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. compared with other feature clustering methods (ex. supervised feature clustering), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labels over (seen) features available in training data. thus, it can deal with both seen and unseen features in feature clustering process. our experimental results show that feature clustering can aggressively reduce the dimensionality of feature space, while still maintaining state of the art sense disambiguation accuracy. furthermore, when combined with a semi-supervised wsd algorithm, semi-supervised feature clustering outperforms other dimensionality reduction techniques, which indicates that using unlabeled data in learning process helps to improve the performance of feature clustering and sense disambiguation.
learning what to talk about in descriptive games. text generation requires a planning module to select an object of discourse and its properties. this is specially hard in descriptive games, where a computer agent tries to describe some aspects of a game world. we propose to formalize this problem as a markov decision process, in which an optimal message policy can be defined and learned through simulation. furthermore, we propose back-off policies as a novel and effective technique to fight state dimensionality explosion in this framework.
mining key phrase translations from web corpora. key phrases are usually among the most information-bearing linguistic structures. translating them correctly will improve many natural language processing applications. we propose a new framework to mine key phrase translations from web corpora. we submit a source phrase to a search engine as a query, then expand queries by adding the translations of topic-relevant hint words from the returned snippets. we retrieve mixed-language web pages based on the expanded queries. finally, we extract the key phrase translation from the second-round returned web page snippets with phonetic, semantic and frequency-distance features. we achieve 46% phrase translation accuracy when using top 10 returned snippets, and 80% accuracy with 165 snippets. both results are significantly better than several existing methods.
blanc: learning evaluation metrics for mt. we introduce blanc, a family of dynamic, trainable evaluation metrics for machine translation. flexible, parametrized models can be learned from past data and automatically optimized to correlate well with human judgments for different criteria (e.g. adequacy, fluency) using different correlation measures. towards this end, we discuss acs (all common skip-ngrams), a practical algorithm with trainable parameters that estimates reference-candidate translation overlap by computing a weighted sum of all common skip-ngrams in polynomial time. we show that the bleu and rouge metric families are special cases of blanc, and we compare correlations with human judgments across these three metric families. we analyze the algorithmic complexity of acs and argue that it is more powerful in modeling both local meaning and sentence-level structure, while offering the same practicality as the established algorithms it generalizes.
speeding up training with tree kernels for node relation labeling. we present a method for speeding up the calculation of tree kernels during training. the calculation of tree kernels is still heavy even with efficient dynamic programming (dp) procedures. our method maps trees into a small feature space where the inner product, which can be calculated much faster, yields the same value as the tree kernel for most tree pairs. the training is sped up by using the dp procedure only for the exceptional pairs. we describe an algorithm that detects such exceptional pairs and converts trees into vectors in a feature space. we propose tree kernels on marked labeled ordered trees and show that the training of svms for semantic role labeling using these kernels can be sped up by a factor of several tens.
training neural network language models on very large corpora. during the last years there has been growing interest in using neural networks for language modeling. in contrast to the well known back-off n-gram language models, the neural network approach attempts to overcome the data sparseness problem by performing the estimation in a continuous space. this type of language model was mostly used for tasks for which only a very limited amount of in-domain training data is available.in this paper we present new algorithms to train a neural network language model on very large text corpora. this makes possible the use of the approach in domains where several hundreds of millions words of texts are available. the neural network language model is evaluated in a state-of-the-art real-time continuous speech recognizer for french broadcast news. word error reductions of 0.5% absolute are reported using only a very limited amount of additional processing time.
collective content selection for concept-to-text generation. a content selection component determines which information should be conveyed in the output of a natural language generation system. we present an efficient method for automatically learning content selection rules from a corpus and its related database. our modeling framework treats content selection as a collective classification problem, thus allowing us to capture contextual dependencies between input items. experiments in a sports domain demonstrate that this approach achieves a substantial improvement over context-agnostic methods.
on coreference resolution performance metrics. the paper proposes a constrained entity-alignment f-measure (ceaf) for evaluating coreference resolution. the metric is computed by aligning reference and system entities (or coreference chains) with the constraint that a system (reference) entity is aligned with at most one reference (system) entity. we show that the best alignment is a maximum bipartite matching problem which can be solved by the kuhn-munkres algorithm. comparative experiments are conducted to show that the widely-known muc f-measure has serious flaws in evaluating a coreference system. the proposed metric is also compared with the ace-value, the official evaluation metric in the automatic content extraction (ace) task, and we conclude that the proposed metric possesses some properties such as symmetry and better interpretability missing in the ace-value.
domain-specific sense distributions and predominant sense acquisition. distributions of the senses of words are often highly skewed. this fact is exploited by word sense disambiguation (wsd) systems which back off to the predominant sense of a word when contextual clues are not strong enough. the domain of a document has a strong influence on the sense distribution of words, but it is not feasible to produce large manually annotated corpora for every domain of interest. in this paper we describe the construction of three sense annotated corpora in different domains for a sample of english words. we apply an existing method for acquiring predominant sense information automatically from raw text, and for our sample demonstrate that (1) acquiring such information automatically from a mixed-domain corpus is more accurate than deriving it from semcor, and (2) acquiring it automatically from text in the same domain as the target domain performs best by a large margin. we also show that for an all words wsd task this automatic method is best focussed on words that are salient to the domain, and on words with a different acquired predominant sense in that domain compared to that acquired from a balanced corpus.
evita: a robust event recognizer for qa systems. we present evita, an application for recognizing events in natural language texts. although developed as part of a suite of tools aimed at providing question answering systems with information about both temporal and intensional relations among events, it can be used independently as an event extraction tool. it is unique in that it is not limited to any pre-established list of relation types (events), nor is it restricted to a specific domain. evita performs the identification and tagging of event expressions based on fairly simple strategies, informed by both linguistic-and statistically-based data. it achieves a performance ratio of 80.12% f-measure.