waashk/dblp huggingface dataset:
elaboration in object descriptions through examples. examples are often used along with textual descriptions to help convey particular ideas - especially in instructional or explanatory contexts. these accompanying examples reflect information in the surrounding text, and in turn, also influence the text. sometimes, examples replace possible (textual) elaborations in the description. it is thus clear that if object descriptions are to be generated, the system must incorporate strategies to handle examples. in this work, we shall investigate some of these issues in the generation of object descriptions.
a uniform treatment of pragmatic inferences in simple and complex utterances and sequences of utterances. drawing appropriate defeasible inferences has been proven to be one of the most pervasive puzzles of natural language processing and a recurrent problem in pragmatics. this paper provides a theoretical framework, called stratified logic, that can accommodate defeasible pragmatic inferences. the framework yields an algorithm that computes the conversational, conventional, scalar, clausal, and normal state implicatures; and the presuppositions that are associated with utterances. the algorithm applies equally to simple and complex utterances and sequences of utterances.
packing of feature structures for efficient unification of disjunctive feature structures. this paper proposes a method for packing feature structures, which automatically collapses equivalent parts of lexical/phrasal feature structures of hpsg into a single <i>packed feature structure</i>. this method avoids redundant repetition of unification of those parts. preliminary experiments show that this method can significantly improve a unification speed in parsing.
bottom-up parsing extending context-freeness in a process grammar processor. a new approach to bottom-up parsing that extends augmented context-free grammar to a process grammar is formally presented. a processor grammar (pg) defines a set of rules suited for bottom-up parsing and conceived as processes that are applied by a pg processor. the matching phase is a crucial step for process application, and a parsing structure for efficient matching is also presented. the pg processor is composed of a process scheduler that allows immediate constituent analysis of structures, and behaves in a non-deterministic fashion. on the other side, the pg offers means for implementing specific parsing strategies improving the lack of determinism innate in the processor.
semantic retrieval for the accurate identification of relational concepts in massive textbases. this paper introduces a novel framework for the accurate retrieval of relational concepts from huge texts. prior to retrieval, all sentences are annotated with predicate argument structures and ontological identifiers by applying a deep parser and a term recognizer. during the run time, user requests are converted into queries of region algebra on these annotations. structural matching with pre-computed semantic annotations establishes the accurate and efficient retrieval of relational concepts. this framework was applied to a text retrieval system for medline. experiments on the retrieval of biomedical correlations revealed that the cost is sufficiently small for real-time applications and that the retrieval precision is significantly improved.
a flexible pos tagger using an automatically acquired language model. we present an algorithm that automatically learns context constraints using statistical decision trees. we then use the acquired constraints in a flexible pos tagger. the tagger is able to use information of any degree: n-grams, automatically learned context constraints, linguistically motivated manually written constraints, etc. the sources and kinds of constraints are unrestricted, and the language model can be easily extended, improving the results. the tagger has been tested and evaluated on the wsj corpus.
the structure and performance of an open-domain question answering system. this paper presents the architecture, operation and results obtained with the lasso question answering system developed in the natural language processing laboratory at smu. to find answers, the system relies on a combination of syntactic and semantic techniques. the search for the answer is based on a novel form of indexing called paragraph indexing. a score of 55.5% for short answers and 64.5% for long answers was achieved at the trec-8 competition.
compact representations by finite-state transducers. finite-state transducers give efficient representations of many natural language phenomena. they allow to account for complex lexicon restrictions encountered, without involving the use of a large set of complex rules difficult to analyze. we here show that these representations can be made very compact, indicate how to perform the corresponding minimization, and point out interesting linguistic side-effects of this operation.
dynamic compilation of weighted context-free grammars. weighted context-free grammars are a convenient formalism for representing grammatical constructions and their likelihoods in a variety of language-processing applications. in particular, speech understanding applications require appropriate grammars both to constrain speech recognition and to help extract the meaning of utterances. in many of those applications, the actual languages described are regular, but context-free representations are much more concise and easier to create. we describe an efficient algorithm for compiling into weighted finite automata an interesting class of weighted context-free grammars that represent regular languages. the resulting automata can then be combined with other speech recognition components. our method allows the recognizer to dynamically activate or deactivate grammar rules and substitute a new regular language for some terminal symbols, depending on previously recognized inputs, all without recompilation. we also report experimental results showing the practicality of the approach.
an efficient compiler for weighted rewrite rules. context-dependent rewrite rules are used in many areas of natural language and speech processing. work in computational phonology has demonstrated that, given certain conditions, such rewrite rules can be represented as finite-state transducers (fsts). we describe a new algorithm for compiling rewrite rules into fsts. we show the algorithm to be simpler and more efficient than existing algorithms. further, many of our applications demand the ability to compile weighted rules into weighted fsts, transducers generalized by providing transitions with weights. we have extended the algorithm to allow for this.
applying explanation-based learning to control and speeding-up natural language generation. this paper presents a method for the automatic extraction of subgrammars to control and speeding-up natural language generation nlg. the method is based on explanation-based learning ebl. the main advantage for the proposed new method for nlg is that the complexity of the grammatical decision making process during nlg can be vastly reduced, because the ebl method supports the adaption of a nlg system to a particular use of a language.
performance issues and error analysis in an open-domain question answering system. this paper presents an in-depth analysis of a state-of-the-art question answering system. several scenarios are examined: (1) the performance of each module in a serial baseline system, (2) the impact of feedbacks and the insertion of a logic prover, and (3) the impact of various lexical resources. the main conclusion is that the overall performance depends on the depth of natural language processing resources and the tools used for answer finding.
examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. this paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. we first demonstrate that review identification can be performed with high accuracy using only unigrams as features. we then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.
logic form transformation of wordnet and its applicability to question answering. wordnet is a rich source of world knowledge from which formal axioms can be derived. in this paper we present a method for transforming the wordnet glosses into logic forms and further into axioms. the transformation of wordnet glosses into logic forms is useful for theorem proving and other applications. the paper demonstrates the utility of the wordnet axioms in a question answering system to rank and extract answers.
machine learning for coreference resolution: from local classification to global ranking. in this paper, we view coreference resolution as a problem of ranking candidate partitions generated by different coreference systems. we propose a set of partition-based features to learn a ranking model for distinguishing good and bad partitions. our approach compares favorably to two state-of-the-art coreference systems when evaluated on three standard coreference data sets.
finding ideographic representations of japanese names written in latin script via language identification and corpus validation. multilingual applications frequently involve dealing with proper names, but names are often missing in bilingual lexicons. this problem is exacerbated for applications involving translation between latin-scripted languages and asian languages such as chinese, japanese and korean (cjk) where simple string copying is not a solution. we present a novel approach for generating the ideographic representations of a cjk name written in a latin script. the proposed approach involves first identifying the origin of the name, and then back-transliterating the name to all possible chinese characters using language-specific mappings. to reduce the massive number of possibilities for computation, we apply a three-tier filtering process by filtering first through a set of attested bigrams, then through a set of attested terms, and lastly through the www for a final validation. we illustrate the approach with english-to-japanese back-transliteration. against test sets of japanese given names and surnames, we have achieved average precisions of 73% and 90%, respectively.
multilingual document clustering: an heuristic approach based on cognate named entities. this paper presents an approach for multilingual document clustering in comparable corpora. the algorithm is of heuristic nature and it uses as unique evidence for clustering the identification of cognate named entities between both sides of the comparable corpora. one of the main advantages of this approach is that it does not depend on bilingual or multilingual resources. however, it depends on the possibility of identifying cognate named entities between the languages used in the corpus. an additional advantage of the approach is that it does not need any information about the right number of clusters; the algorithm calculates it. we have tested this approach with a comparable corpus of news written in english and spanish. in addition, we have compared the results with a system which translates selected document features. the obtained results are encouraging.
a hierarchical bayesian language model based on pitman-yor processes. we propose a new hierarchical bayesian n-gram model of natural languages. our model makes use of a generalization of the commonly used dirichlet distributions called pitman-yor processes which produce power-law distributions more closely resembling those in natural languages. we show that an approximation to the hierarchical pitman-yor language model recovers the exact formulation of interpolated kneser-ney, one of the best smoothing methods for n-gram language models. experiments verify that our model gives cross entropy results superior to interpolated kneser-ney and comparable to modified kneser-ney.
is it correct? - towards web-based evaluation of automatic natural language phrase generation. this paper describes a novel approach for the automatic generation and evaluation of a trivial dialogue phrases database. a trivial dialogue phrase is defined as an expression used by a chatbot program as the answer of a user input. a transfer-like genetic algorithm (ga) method is used to generating the trivial dialogue phrases for the creation of a natural language generation (nlg) knowledge base. the automatic evaluation of a generated phrase is performed by producing n-grams and retrieving their frequencies from the world wide web (www). preliminary experiments show very positive results.
integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach. in this paper, we present a new approach for word sense disambiguation (wsd) using an exemplar-based learning algorithm. this approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. we tested our wsd program, named lexas, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. lexas achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of wordnet.
investigations on event-based summarization. we investigate independent and relevant event-based extractive mutli-document summarization approaches. in this paper, events are defined as event terms and associated event elements. with independent approach, we identify important contents by frequency of events. with relevant approach, we identify important contents by pagerank algorithm on the event map constructed from documents. experimental results are encouraging.
learning to recognize tables in free text. many real-world texts contain tables. in order to process these texts correctly and extract the information contained within the tables, it is important to identify the presence and structure of tables. in this paper, we present a new approach that learns to recognize tables in free text, including the boundary, rows and columns of tables. when tested on wall street journal news documents, our learning approach outperforms a deterministic table recognition algorithm that identifies table recognition algorithm that identifies tables based on a fixed set of conditions. our learning approach is also more flexible and easily adaptable to texts in different domains with different table characteristics.
improving ibm word alignment model 1. we investigate a number of simple methods for improving the word-alignment accuracy of ibm model 1. we demonstrate reduction in alignment error rate of approximately 30% resulting from (1) giving extra weight to the probability of alignment to the null word, (2) smoothing probability estimates for rare words, and (3) using a simple heuristic estimation method to initialize, or replace, em training of model parameters.
exploiting parallel texts for word sense disambiguation: an empirical study. a central problem of word sense disambiguation (wsd) is the lack of manually sense-tagged data required for supervised learning. in this paper, we evaluate an approach to automatically acquire sense-tagged training data from english-chinese parallel corpora, which are then used for disambiguating the nouns in the senseval-2 english lexical sample task. our investigation reveals that this method of acquiring sense-tagged data is promising. on a subset of the most difficult senseval-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. our analysis also highlights the importance of the issue of domain dependence in evaluating wsd programs.
unification-based semantic interpretation. we show how unification can be used to specify the semantic interpretation of natural-language expressions, including problematical constructions involving long-distance dependencies. we also sketch a theoretical foundation for unification-based semantic interpretation, and compare the unification-based approach with more conventional techniques based on the lambda calculus.
rule writing or annotation: cost-efficient resource usage for base noun phrase chunking. this paper presents a comprehensive empirical comparison between two approaches for developing a base noun phrase chunker: human rule writing and active learning using interactive real-time human annotation. several novel variations on active learning are investigated, and underlying cost models for cross-modal machine learning comparison are presented and explored. results show that it is more efficient and more successful by several measures to train a system using active learning annotation rather than hand-crafted rule writing at a comparable level of human labor investment.
semantic parsing with structured svm ensemble classification models. we present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. we also propose a selection method which is based on a switching model among a set of outputs of individual classifiers when dealing with natural language parsing problems. the switching model uses subtrees mined from the corpus and a boosting-based algorithm to select the most appropriate output. the application of the proposed framework on the domain of semantic parsing shows advantages in comparison with the original large margin methods.
planning text for advisory dialogues. explanation is an interactive process requiring a dialogue between advice-giver and advice-seeker. in this paper, we argue that in order to participate in a dialogue with its users, a generation system must be capable of reasoning about its own utterances and therefore must maintain a rich representation of the responses it produces. we present a text planner that constructs a detailed text plan, containing the intentional, attentional, and rhetorical structures of the text it generates.
word alignment and cross-lingual resource acquisition. annotated corpora are valuable resources for developing natural language processing applications. this work focuses on acquiring annotated data for multilingual processing applications. we present an annotation environment that supports a web-based user-interface for acquiring word alignments between english and chinese as well as a visualization tool for researchers to explore the annotated data.
improved discriminative bilingual word alignment. for many years, statistical machine translation relied on generative models to provide bilingual word alignments. in 2005, several independent efforts showed that discriminative models could be used to enhance or replace the standard generative approach. building on this work, we demonstrate substantial improvement in word-alignment accuracy, partly though improved training methods, but predominantly through selection of more and better features. our best model produces the lowest alignment error rate yet reported on canadian hansards bilingual data.
a uniform approach to underspecification and parallelism. we propose a unified framework in which to treat semantic underspecification and parallelism phenomena in discourse. the framework employs a constraint language that can express equality and subtree relations between finite trees. in addition, our constraint language can express the equality up-to relation over trees which captures parallelism between them. the constraints are solved by context unification. we demonstrate the use of our framework at the examples of quantifier scope, ellipsis, and their interaction.
quantifier scoping in the sri core language engine. an algorithm for generating the possible quantifier scopings for a sentence, in order of preference, is outlined. the scoping assigned to a quantifier is determined by its interactions with other quantifiers, modals, negation, and certain syntactic-constituent boundaries. when a potential scoping is logically equivalent to another, the less preferred one is discarded.the relative scoping preferences of the individual quantifiers are not embedded in the algorithm, but are specified by a set of rules. many of the rules presented here have appeared in the linguistics literature and have been used in various natural language processing systems. however, the co-ordination of these rules and the resulting coverage represents a significant contribution. because experimental data on human quantifier-scoping preferences are still fragmentary, we chose to design a system in which the set of preference rules could be easily modified and expanded.the algorithm described has been implemented in prolog as part of a larger natural language processing system. extensions of this algorithm are in progress.
bridging the gap between underspecification formalisms: minimal recursion semantics as dominance constraints. minimal recursion semantics (mrs) is the standard formalism used in large-scale hpsg grammars to model underspecified semantics. we present the first provably efficient algorithm to enumerate the readings of mrs structures, by translating them into normal dominance constraints.
representing constraints with automata. in this paper we describe an approach to constraint based syntactic theories in terms of finite tree automata. the solutions to constraints expressed in weak monadic second order (mso) logic are represented by tree automata recognizing the assignments which make the formulas true. we show that this allows an efficient representation of knowledge about the content of constraints which can be used as a practical tool for grammatical theory verification. we achieve this by using the intertranslatability of formulae of mso logic and tree automata and the embedding of mso logic into a constraint logic programming scheme. the usefulness of the approach is discussed with examples from the realm of principles-and-parameters based parsing.
a dp based search algorithm for statistical machine translation. we introduce a novel search algorithm for statistical machine translation based on dynamic programming (dp). during the search process two statistical knowledge sources are combined: a translation model and a bigram language model. this search algorithm expands hypotheses along the positions of the target string while guaranteeing progressive coverage of the words in the source string. we present experimental results on the verbmobil task.
a stochastic language model using dependency and its improvement by word clustering. in this paper, we present a stochastic language model for japanese using dependency. the prediction unit in this model is an attribute of "bunsetsu". this is represented by the product of the head of content words and that of function words. the relation between the attributes of "bunsetsu" is ruled by a context-free grammar. the word sequences are predicted from the attribute using word n-gram model. the spell of unknow word is predicted using character n-gram model. this model is robust in that it can compute the probability of an arbitrary string and is complete in that it models from unknown word to dependency at the same time.