waashk/dblp huggingface dataset:
natural language access to software applications. this paper reports on the esprit project melissa (methods and tools for natural-language interfacing with standard software applications)1. melissa aims at developing the technology and tools enabling end users to interface with computer applications, using natural-language (nl), and to obtain a precompetitive product validated in selected enduser applications. this paper gives an overview of the approach to solving (nl) interfacing problem and outlines some of the methods and software components developed in the project.
contrastive estimation: training log-linear models on unlabeled data. conditional random fields (lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (sha and pereira, 2003) and named-entity extraction (mccallum and li, 2003). crfs are log-linear, allowing the incorporation of arbitrary features into the model. to train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. we describe a novel approach, contrastive estimation. we show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. applied to a sequence labeling problem---pos tagging given a tagging dictionary and unlabeled text---contrastive estimation outperforms em (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.
recognizing syntactic errors in the writing of second language learners. this paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard english. the system models the grammar of the learner, with this instantiation of the system tailored to signers of american sign language (asl). we discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. our method of capturing ungrammaticalities involves using mal-rules (also called 'error productions'). however, the straightforward addition of some mal-rules causes significant performance problems with the parser. for instance, the asl population has a strong tendency to drop pronouns and the auxiliary verb 'to be'. being able to account for these as sentences results in an explosion in the number of possible parses for each sentence. this explosion, left unchecked, greatly hampers the performance of the system. we discuss how this is handled by taking into account expectations from the specific population (some of which are captured in our unique user model). the different representations of lexical items at various points in the acquisition process are modeled by using mal-rules, which obviates the need for multiple lexicons. the grammar is evaluated on its ability to correctly diagnose agreement problems in actual sentences produced by asl native speakers.
annealing structural bias in multilingual weighted grammar induction. we first show how a structural locality bias can improve the accuracy of state-of-the-art dependency grammar induction models trained by em from unannotated examples (klein and manning, 2004). next, by annealing the free parameter that controls this bias, we achieve further improvements. we then describe an alternative kind of structural bias, toward "broken" hypotheses consisting of partial structures over segmented sentences, and show a similar pattern of improvement. we relate this approach to contrastive estimation (smith and eisner, 2005a), apply the latter to grammar induction in six languages, and show that our new approach improves accuracy by 1-17% (absolute) over ce (and 8-30% over em), achieving to our knowledge the best results on this task to date. our method, structural annealing, is a general technique with broad applicability to hidden-structure discovery problems.
atlas - a new text alignment architecture. we are presenting a new, hybrid alignment architecture for aligning bilingual, linguistically annotated parallel corpora. it is able to align simultaneously at paragraph, sentence, phrase and word level, using statistical and heuristic cues, along with linguistics-based rules. the system currently aligns english and german texts, and the linguistic annotation used covers pos-tags, lemmas and syntactic constitutents. however, as the system is highly modular, we can easily adapt it to new language pairs and other types of annotation. the hybrid nature of the system allows experiments with a variety of alignment cues to find solutions to word alignment problems like the correct alignment of rare words and multiwords, or how to align despite syntactic differences between two languages. first performance tests are promising, and we are setting up a gold standard for a thorough evaluation of the system.
minimum risk annealing for training log-linear models. when training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. since the error surface for many natural language problems is piecewise constant and riddled with local minima, many systems instead optimize log-likelihood, which is conveniently differentiable and convex. we propose training instead to minimize the expected loss, or risk. we define this expectation using a probability distribution over hypotheses that we gradually sharpen (anneal) to focus on the 1-best hypothesis. besides the linear loss functions used in previous work, we also describe techniques for optimizing nonlinear functions such as precision or the bleu metric. we present experiments training log-linear combinations of models for dependency parsing and for machine translation. in machine translation, annealed minimum risk training achieves significant improvements in bleu over standard minimum error training. we also show improvements in labeled dependency parsing.
computational properties of environment-based disambiguation. the standard pipeline approach to semantic processing, in which sentences are morphologically and syntactically resolved to a single tree before they are interpreted, is a poor fit for applications such as natural language interfaces. this is because the environment information, in the form of the objects and events in the application's runtime environment, cannot be used to inform parsing decisions unless the input sentence is semantically analyzed, but this does not occur until after parsing in the single-tree semantic architecture. this paper describes the computational properties of an alternative architecture, in which semantic analysis is performed on all possible interpretations during parsing, in polynomial time.
using model-theoretic semantic interpretation to guide statistical parsing and word recognition in a spoken language interface. this paper describes an extension of the semantic grammars used in conventional statistical spoken language interfaces to allow the probabilities of derived analyses to be conditioned on the meanings or denotations of input utterances in the context of an interface's underlying application environment or world model. since these denotations will be used to guide disambiguation in interactive applications, they must be efficiently shared among the many possible analyses that may be assigned to an input utterance. this paper therefore presents a formal restriction on the scope of variables in a semantic grammar which guarantees that the denotations of all possible analyses of an input utterance can be calculated in polynomial time, without undue constraints on the expressivity of the derived semantics. empirical tests show that this model-theoretic interpretation yields a statistically significant improvement on standard measures of parsing accuracy over a baseline grammar not conditioned on denotations.
unsupervised induction of modern standard arabic verb classes using syntactic frames and lsa. we exploit the resources in the arabic treebank (atb) and arabic gigaword (ag) to determine the best features for the novel task of automatically creating lexical semantic verb classes for modern standard arabic (msa). the verbs are classified into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. the results of the clustering experiments are compared with a gold standard set of classes, which is approximated by using the noisy english translations provided in the atb to create levin-like classes for msa. the quality of the clusters is found to be sensitive to the inclusion of syntactic frames, lsa vectors, morphological pattern, and subject animacy. the best set of parameters yields an f&beta;=1 score of 0.456, compared to a random baseline of an f&beta;=1 score of 0.205.
preserving semantic dependencies in synchronous tree adjoining grammar. rambow, wier and vijay-shanker (rambow et al., 1995) point out the differences between tag derivation structures and semantic or predicate-argument dependencies, and joshi and vijay-shanker (joshi and vijay-shanker, 1999) describe a monotonic compositional semantics based on attachment order that represents the desired dependencies of a derivation without underspecifying predicate-argument relationships at any stage. in this paper, we apply the joshi and vijay-shanker conception of compositional semantics to the problem of preserving semantic dependencies in synchronous tag translation (shieber and schabes, 1990; abeill&eacute; et al., 1990). in particular, we describe an algorithm to obtain the semantic dependencies on a tag parse forest and construct a target derivation forest with isomorphic or locally non-isomorphic dependencies in <i>o (n<sup>7</sup>)</i> time.
a bayesian model for morpheme and paradigm identification. this paper describes a system for unsupervised learning of morphological affixes from texts or word lists. the system is composed of a generative probability model and a search algorithm. experiments on the wall street journal and the hansard corpus (french and english) demonstrate the effectiveness of this approach. the results suggest that more integrated systems for learning both affixes and morphographemic adjustment rules may be feasible. in addition, several definitions and a theorem are developed so that our search algorithm can be formalized in terms of the lattice formed by subsets of suffixes under inclusion. this formalism is expected to be useful for investigating alternative search strategies over the same morphological hypothesis space.
multi-component tag and notions of formal power. this paper presents a restricted version of set-local multi-component tags (weir, 1988) which retains the strong generative capacity of tree-local multi-component tag (i.e. produces the same derived structures) but has a greater derivational generative capacity (i.e. can derive those structures in more ways). this formalism is then applied as a framework for integrating dependency and constituency based linguistic representations.
semantic taxonomy induction from heterogenous evidence. we propose a novel algorithm for inducing semantic taxonomies. previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. by contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. we apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (wordnet 2.1). we add 10,000 novel synsets to wordnet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. finally, we show that a taxonomy built using our algorithm shows a 23% relative f-score improvement over wordnet 2.1 on an independent testset of hypernym pairs.
exploiting named entity taggers in a second language. in this work we present a method for named entity recognition (ner). our method does not rely on complex linguistic resources, and apart from a hand coded system, we do not use any language-dependent tools. the only information we use is automatically extracted from the documents, without human intervention. moreover, the method performs well even without the use of the hand coded system. the experimental results are very encouraging. our approach even outperformed the hand coded system on ner in spanish, and it achieved high accuracies in portuguese.
part-of-speech induction from scratch. this paper presents a method for inducing the parts of speech of a language and part-of-speech labels for individual words from a large text corpus. vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. a dimensionality reduction creates a space representing the syntactic categories of unambiguous words. a neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. the method classifies both ambiguous and unambiguous words correctly with high accuracy.
part-of-speech tagging using a variable memory markov model. we present a new approach to disambiguating syntactically ambiguous words in context, based on variable memory markov (vmm) models. in contrast to fixed-length markov models, which predict based on fixed-lenth histories, variable memory markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. in a test of a vmm based tagger on the brown corpus, 95.81% of tokens are correctly classified.
k-qard: a practical korean question answering framework for restricted domain. we present a korean question answering framework for restricted domains, called k-qard. k-qard is developed to achieve domain portability and robustness, and the framework is successfully applied to build question answering systems for several domains.
reading level assessment using support vector machines and statistical language models. reading proficiency is a fundamental component of language competency. however, finding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. this task can be addressed with natural language processing technology to assess reading level. existing measures of reading level are not well suited to this task, but previous work and our own pilot experiments have shown the benefit of using statistical language models. in this paper, we also use support vector machines to combine features from traditional reading level measures, statistical language models, and other language processing tools to produce a better method of assessing reading level.
a connectionist approach to propositional phrase attachment for real world texts. in this paper we describe a neural network-based approach to prepositional phrase attachment disam biguation for real world texts. although the use of semantic classes in this task seems intuitively to be adequate, methods employed to date have not used them very effectively. causes of their poor results are discussed. our model, which uses only classes, scores appreciably better than the other class-based methods which have been tested on the wall street journal corpus. to date, the best result obtained using only classes was a score of 79.1%; we obtained an accuracy score of 86.8%. this score is among the best reported in the literature using this corpus.
continuous space language models for statistical machine translation. statistical machine translation systems are based on one or more translation models and a language model of the target language. while many different translation models and phrase extraction algorithms have been proposed, a standard word n-gram back-off language model is used in most systems. in this work, we propose to use a new statistical language model that is based on a continuous representation of the words in the vocabulary. a neural network is used to perform the projection and the probability estimation. we consider the translation of european parliament speeches. this task is part of an international evaluation organized by the tc-star project in 2006. the proposed method achieves consistent improvements in the bleu score on the development and test data. we also present algorithms to improve the estimation of the language model probabilities when splitting long sentences into shorter chunks.
towards developing generation algorithms for text-to-text applications. we describe a new sentence realization framework for text-to-text applications. this framework uses idl-expressions as a representation formalism, and a generation mechanism based on algorithms for intersecting idl-expressions with probabilistic language models. we present both theoretical and empirical results concerning the correctness and efficiency of these algorithms.
parallel multiple context-free grammars, finite-state translation systems, and polynomial-time recognizable subclasses of lexical-functional grammars. a number of grammatical formalisms were introduced to define the syntax of natural languages. among them are parallel multiple context-free grammars (pmcfg's) and lexical-functional grammars (lfg's). pmcfg's and their subclass called multiple context-free grammars (mcfg's) are natural extensions of cfg's, and pmcfg's are known to be recognizable in polynomial time. some subclasses of lfg's have been proposed, but they were shown to generate an np-complete language. finite state translation systems (fts') were introduced as a computational model of transformational grammars. in this paper, three subclasses of lfg's called nc-lfg's, dc-lfg's and fc-lfg's are introduced and the generative capacities of the above mentioned grammatical formalisms are investigated. first, we show that the generative capacity of fts' is equal to that of nc-lfg's. as relations among subclasses of those formalisms, it is shown that the generative capacities of deterministic fts', dc-lfg's, and pmcfg's are equal to each other, and the generative capacity of fc-lfg's is equal to that of mcfg's. it is also shown that at least one np-complete language is generated by fts'. consequently, deterministic fts', dc-lfg's and fc-lfg's can be recognized in polynomial time. however, fts' (and nc-lfg's) cannot, if p &ne; np.
stochastic language generation using widl-expressions and its application in machine translation and summarization. we propose widl-expressions as a flexible formalism that facilitates the integration of a generic sentence realization system within end-to-end language processing applications. widl-expressions represent compactly probability distributions over finite sets of candidate realizations, and have optimal algorithms for realization via interpolation with language model probability distributions. we show the effectiveness of a widl-based nlg system in two sentence realization tasks: automatic translation and headline generation.
on-demand information extraction. at present, adapting an information extraction system to new topics is an expensive and slow process, requiring some knowledge engineering for each new topic. we propose a new paradigm of information extraction which operates 'on demand' in response to a user's query. on-demand information extraction (odie) aims to completely eliminate the customization effort. given a user's query, the system will automatically create patterns to extract salient relations in the text of the topic, and build tables from the extracted information using paraphrase discovery technology. it relies on recent advances in pattern discovery, paraphrase discovery, and extended named entity tagging. we report on experimental results in which the system created useful tables for many topics, demonstrating the feasibility of this approach.
discourse generation using utility-trained coherence models. we describe a generic framework for integrating various stochastic models of discourse coherence in a manner that takes advantage of their individual strengths. an integral part of this framework are algorithms for searching and training these stochastic coherence models. we evaluate the performance of our models and algorithms and show empirically that utility-trained log-linear coherence models outperform each of the individual coherence models considered.
transforming lattices into non-deterministic automata with optional null arcs. the problem of transforming a lattice into a non-deterministic finite state automaton is non-trivial. we present a transformation algorithm which tracks, for each node of an automaton under construction, the larcs which it reflects and the lattice nodes at their origins and extremities. an extension of the algorithm permits the inclusion of null, or epsilon, arcs in the output automaton. the algorithm has been successfully applied to lattices derived from dictionaries, i.e. very large corpora of strings.
semantics of conceptual graphs. conceptual graphs are both a language for representing knowledge and patterns for constructing models. they form models in the ai sense of structures that approximate some actual or possible system in the real world. they also form models in the logical sense of structures for which some set of axioms are true. when combined with recent developments in nonstandard logic and semantics, conceptual graphs can form a bridge between heuristic techniques of ai and formal techniques of model theory.
locating noun phrases with finite state transducers. we present a method for constructing, maintaining and consulting a database of proper nouns. we describe noun phrases composed of a proper noun and/or a description of a human occupation. they are formalized by finite state transducers (fst) and large coverage dictionaries and are applied to a corpus of newspapers. we take into account synonymy and hyperonymy. this first stage of our parsing procedure has a high degree of accuracy. we show how we can handle requests such as: 'find all newspaper articles in a general corpus mentioning the french prime minister', or 'how is mr. x referred to in the corpus; what have been his different occupations through out the period over which our corpus extends?' in the first case, non trivial occurrences of noun phrases are located, that is phrases not containing words present in the request, but either synonyms, or proper nouns relevant to request. the results of the search is far better than than those obtained by a key-word based engine. most answers are correct: except some cases of homonymy (where a human reader would also fail without more context). also, the treatment of people having several different occupations is not fully resolved. we have built for french, a library of about one thousand such fsts, and english fsts are under construction. the same method can be used to locate and propose new proper nouns, simply by replacing given proper names in the same fsts by variables.
a hybrid relational approach for wsd - first results. we present a novel hybrid approach for word sense disambiguation (wsd) which makes use of a relational formalism to represent instances and background knowledge. it is built using inductive logic programming techniques to combine evidence coming from both sources during the learning process, producing a rule-based wsd model. we experimented with this approach to disambiguate 7 highly ambiguous verbs in english-portuguese translation. results showed that the approach is promising, achieving an average accuracy of 75%, which outperforms the other machine learning techniques investigated (66%).