waashk/dblp huggingface dataset:
learning surface text patterns for a question answering system. in this paper we explore the power of surface text patterns for open-domain question answering systems. in order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. a tagged corpus is built from the internet in a bootstrapping process by providing a few hand-crafted examples of each question type to altavista. patterns are then automatically extracted from the returned documents and standardized. we calculate the precision of each pattern, and the average precision for each question type. these patterns are then applied to find answers to new questions. using the trec-10 question set, we report results for two cases: answers determined from the trec-10 corpus and from the web.
word sense disambiguation by learning from unlabeled data. most corpus-based approaches to natural language processing suffer from lack of training data. this is because acquiring a large number of labeled data is expensive. this paper describes a learning method that exploits unlabeled data to tackle data sparseness problem. the method uses committee learning to predict the labels of unlabeled data that augment the existing training data. our experiments on word sense disambiguation show that predictive accuracy is significantly improved by using additional unlabeled data.
randomized algorithms and nlp: using locality sensitive hash functions for high speed noun clustering. in this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. we apply these algorithms to generate noun similarity lists from 70 million pages. we reduce the running time from quadratic to practically linear in the number of elements to be computed.
disambiguating and interpreting verb definitions. to achieve our goal of building a comprehensive lexical database out of various on-line resources, it is necessary to interpret and disambiguate the information found in these resources. in this paper we describe a disambiguation module which analyzes the content of dictionary definitions, in particular, definitions of the form "to verb with np". we discuss the semantic relations holding between the head and the prepositional phrase in such structures, as well as our heuristics for identifying these relations and for disambiguating the senses of the words involved. we present some results obtained by the disambiguation module and evaluate its rate of success as compared with results obtained from human judgments.
parsing and interpreting comparatives. we describe a fairly comprehensive handling of the syntax and semantics of comparative constructions. the analysis is largely based on the theory developed by pinkham, but we advance arguments to support a different handling of phrasal comparatives - in particular, we use direct interpretation instead of c-ellipsis. we explain the reasons for dividing comparative sentences into different categories, and for each category we give an example of the corresponding montague semantics. the ideas have all been implemented within a large-scale grammar for swedish.
names and similarities on the web: fact extraction in the fast lane. in a new approach to large-scale extraction of facts from unstructured text, distributional similarities become an integral part of both the iterative acquisition of high-coverage contextual extraction patterns, and the validation and ranking of candidate facts. the evaluation measures the quality and coverage of facts extracted from one hundred million web documents, starting from ten seed facts and using no additional knowledge, lexicons or complex tools.
fast parsing using pruning and grammar specialization. we show how a general grammar may be automatically adapted for fast parsing of utterances from a specific domain by means of constituent pruning and grammar specialization based on explanation-based learning. these methods together give an order of magnitude increase in speed, and the coverage loss entailed by grammar specialization is reduced to approximately half that reported in previous work. experiments described here suggest that the loss of coverage has been reduced to the point where it no longer causes significant performance degradation in the context of a real application.
getting at discourse referents. i examine how discourse anaphoric uses of the definite pronoun it contrast with similar uses of the demonstrative pronoun that. their distinct contexts of use are characterized in terms of two contextual features---persistence of grammatical subject and persistence of grammatical form---which together demonstrate very clearly the interrelation among lexical choice, grammatical choices and the dimension of time in signalling the dynamic attentional state of a discourse.
a voice enabled procedure browser for the international space station. clarissa, an experimental voice enabled procedure browser that has recently been deployed on the international space station (iss), is to the best of our knowledge the first spoken dialog system in space. this paper gives background on the system and the iss procedures, then discusses the research developed to address three key problems: grammar-based speech recognition using the regulus toolkit; svm based methods for open microphone speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations.
some facts about centers, indexicals, and demonstratives. certain pronoun contexts are argued to establish a local center (lc), i.e., a conventionalized indexical similar to 1st/2nd pers. pronouns. demonstrative pronouns, also indexicals, are shown to access entities that are not lcs because they lack discourse relevance or because they are not yet in the universe of discourse.
using emoticons to reduce dependency in machine learning techniques for sentiment classification. sentiment classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. this paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
intention-based segmentation: human reliability and correlation with linguistic cues. certain spans of utterances in a discourse, referred to here as segments, are widely assumed to form coherent units. further, the segmental structure of discourse has been claimed to constrain and be constrained by many phenomena. however, there is weak consensus on the nature of segments and the criteria for recognizing or generating them. we present quantitative results of a two part study using a corpus of spontaneous, narrative monologues. the first part evaluates the statistical reliability of human segmentation of our corpus, where speaker intention is the segmentation criterion. we then use the subjects' segmentations to evaluate the correlation of discourse segmentation with three linguistic cues (referential noun phrases, cue words, and pauses), using information retrieval metrics.
jabot: a multilingual java-based intelligent agent for web sites. this paper presents a novel type of intelligent agent with a multilingual natural language interface, which retrieves information from within a web site. this agent, named jabot after the fact that it is a bot which has been programmed in java, has been designed and developed by the authors in an attempt to solve common web site problems related to information retrieval. jabot runs quickly and efficiently, and rather than running directly on the web site pages, it is connected to a lexical semantic map. this map is based upon the contents of the web site in question together with other associated linguistic knowledge.
a computational model of social perlocutions. the view that communication is a form of action serving a variety of specific functions has had a tremendous impact on the philosophy of language and on computational linguistics. yet, this mode of analysis has been applied to only a narrow range of exchanges (e.g. those whose primary purpose is transferring information or coordinating tasks) while exchanges meant to manage interpersonal relationships, maintain "face", or simply to convey thanks, sympathy, and so on have been largely ignored. we present a model of such "social perlocutions" that integrates previous work in natural language generation, social psychology, and communication studies. this model has been implemented in a system that generates socially appropriate e-mail in response to user-specified communicative goals.
generating the structure of argument. this paper demonstrates that generating arguments in natural language requires planning at an abstract level, and that the appropriate abstraction cannot be captured by approaches based solely upon coherence relations. an abstraction based planning system is presented which employs operators motivated by empirical study and rhetorical maxims. these operators include a subset of traditional deductive rules of inference, argumentation theoretic rules of refutation, and inductive reasoning patterns. the paper presents a unified system in which the various argument forms are employed in generating rich, complex structures for persuasive text.
an extensive empirical study of collocation extraction methods. this paper presents a status quo of an ongoing research study of collocations -- an essential linguistic phenomenon having a wide spectrum of applications in the field of natural language processing. the core of the work is an empirical evaluation of a comprehensive list of automatic collocation extraction methods using precision-recall measures and a proposal of a new approach integrating multiple basic methods and statistical classification. we demonstrate that combining multiple independent techniques leads to a significant performance improvement in comparison with individual basic methods.
learning perceptually-grounded semantics in the l project. a method is presented for acquiring perceptually-grounded semantics for spatial terms in a simple visual domain, as a part of the l0 miniature language acquisition project. two central problems in this learning task are (a) ensuring that the terms learned generalize well, so that they can be accurately applied to new scenes, and (b) learning in the absence of explicit negative evidence. solutions to these two problems are presented, and the results discussed.
combining association measures for collocation extraction. we introduce the possibility of combining lexical association measures and present empirical results of several methods employed in automatic collocation extraction. first, we present a comprehensive summary overview of association measures and their performance on manually annotated data evaluated by precision-recall graphs and mean average precision. second, we describe several classification methods for combining association measures, followed by their evaluation and comparison with individual measures. finally, we propose a feature selection algorithm significantly reducing the number of combined measures with only a small performance degradation.
the computational complexity of avoiding conversational implicatures. referring expressions and other object descriptions should be maximal under the local brevity, no unnecessary components, and lexical preference preference rules; otherwise, they may lead hearers to infer unwanted conversational implicatures. these preference rules can be incorporated into a polynomial time generation algorithm, while some alternative formalizations of conversational implicature make the generation task np-hard.
mining metalinguistic activity in corpora to create lexical resources using information extraction techniques: the mop system. this paper describes and evaluates mop, an ie system for automatic extraction of metalinguistic information from technical and scientific documents. we claim that such a system can create special databases to bootstrap compilation and facilitate update of the huge and dynamically changing glossaries, knowledge bases and ontologies that are vital to modern-day research.
using classification to generate text. the idas natural-language generation system uses a kl-one type classifier to perform content determination, surface realisation, and part of text planning. generation-by-classification allows idas to use a single representation and reasoning component for both domain and linguistic knowledge, which is difficult for systems based on unification or systemic generation techniques.
tractability and structural closures in attribute logic type signatures. this paper considers three assumptions conventionally made about signatures in typed feature logic that are in potential disagreement with current practice among grammar developers and linguists working within feature-based frameworks such as hpsg: meet-semi-latticehood, unique feature introduction, and the absence of subtype covering. it also discusses the conditions under which each of these can be tractably restored in realistic grammar signatures where they do not already exist.
using a randomised controlled clinical trial to evaluate an nlg system. the stop system, which generates personalised smoking-cessation letters, was evaluated by a randomised controlled clinical trial. we believe this is the largest and perhaps most rigorous task effectiveness evaluation ever performed on an nlg system. the detailed results of the clinical trial have been presented elsewhere, in the medical literature. in this paper we discuss the clinical trial itself: its structure and cost, what we did and did not learn from it (especially considering that the trial showed that stop was not effective), and how it compares to other nlg evaluation techniques.
generalized encoding of description spaces and its application to typed feature structures. this paper presents a new formalization of a unification- or join-preserving encoding of partially ordered sets that more essentially captures what it means for an encoding to preserve joins, generalizing the standard definition in ai research. it then shows that every statically typable ontology in the logic of typed feature structures can be encoded in a data structure of fixed size without the need for resizing or additional union-find operations. this is important for any grammar implementation or development system based on typed feature structures, as it significantly reduces the overhead of memory management and reference-pointer-chasing during unification.
summarizing multilingual spoken negotiation dialogues. we present the multilingual summarization functionality for verb-mobil, a speech translation system. we reuse resources of the system to create a summary. after content extraction, we interpret the results in the dialog context. a summary generator provides the input to generation. a first evaluation indicates the feasibility of the approach.
balancing clarity and efficiency in typed feature logic through delaying. the purpose of this paper is to re-examine the balance between clarity and efficiency in hpsg design, with particular reference to the design decisions made in the english resource grammar (lingo, 1999, erg). it is argued that a simple generalization of the conventional delay statements used in logic programming is sufficient to restore much of the functionality and concomitant benefit that the erg elected to forego, with an acceptable although still perceptible computational cost.
utilizing statistical dialogue act processing in verbmobil. in this paper, we present a statistical approach for dialogue act processing in the dialogue component of the speech-to-speech translation system verbmobil. statistics in dialogue processing is used to predict follow-up dialogue acts. as an application example we show how it supports repair when unexpected dialogue states occur.
parametric types for typed attribute-value logic. parametric polymorphism has been combined with inclusional polymorphism to provide natural type systems for prolog (dh88), hilog (yfs92), and constraint resolution languages (smo89), and, in linguistics, by hpsg-like grammars to classify lists and sets of linguistic objects (ps94), and by phonologists in representations of hierarchical structure (kle91). this paper summarizes the incorporation of parametric types into the typed attribute-value logic of (car92), thus providing a natural extension to the type system for ale (cp96). following (car92), the concern here is not with models of feature terms themselves, but with how to compute with parametric types, and what diferent kinds of information one can represent relative to a signature with parametric types, than relative to a signature without them. this enquiry has yielded a more flexible interpretation of parametric types with several specific properties necessary to conform to their current usage by linguists and implementors who work with feature-based formalisms.
a tabulation-based parsing method that reduces copying. this paper presents a new bottom-up chart parsing algorithm for prolog along with a compilation procedure that reduces the amount of copying at run-time to a constant number (2) per edge. it has applications to unification-based grammars with very large partially ordered categories, in which copying is expensive, and can facilitate the use of more sophisticated indexing strategies for retrieving such categories that may otherwise be overwhelmed by the cost of such copying. it also provides a new perspective on "quick-checking" and related heuristics, which seems to confirm that forcing an early failure (as opposed to seeking an early guarantee of success) is in fact the best approach to use. a preliminary empirical evaluation of its performance is also provided.
ontologizing semantic relations. many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge repositories. in this paper, we propose two algorithms for automatically ontologizing (attaching) semantic relations into wordnet. we present an empirical evaluation on the task of attaching part-of and causation relations, showing an improvement on f-score over a baseline model.
mining the web for bilingual text. strand (resnik, 1998) is a language-independent system for automatic discovery of text in parallel translation on the world wide web. this paper extends the preliminary strand results by adding automatic language identification, scaling up by orders of magnitude, and formally evaluating performance. the most recent end-product is an automatically acquired parallel corpus comprising 2491 english-french document pairs, approximately 1.5 million words per language.
a structure-sharing representation for unification-based grammar formalisms. this paper describes a structure-sharing method for the representation of complex phrase types in a parser for patr-ii, a unification-based grammar formalism.in parsers for unification-based grammar formalisms, complex phrase types are derived by incremental refinement of the phrase types defined in grammar rules and lexical entries. in a na&iuml;ve implementation, a new phrase type is built by copying older ones and then combining the copies according to the constraints stated in a grammar rule. the structure-sharing method was designed to eliminate most such copying; indeed, practical tests suggest that the use of this technique reduces parsing time by as much as 60%.the present work is inspired by the structure-sharing method for theorem proving introduced by boyer and moore and on the variant of it that is used in some prolog implementations.
the linguist's search engine: an overview. the linguist's search engine (lse) was designed to provide an intuitive, easy-to-use interface that enables language researchers to seek linguistically interesting examples on the web, based on syntactic and lexical criteria. we briefly describe its user interface and architecture, as well as recent developments that include lse search capabilities for chinese.
a calculus for semantic composition and scoping. certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. such reliance on the form rather than the content of semantic interpretations goes against the spirit of compositionality. i will show that those scoping restrictions follow from simple and fundamental facts about functional application and abstraction, and can be expressed as constraints on the derivation of possible meanings for sentences rather than constraints of the alleged forms of those meanings.
inside-outside reestimation from partially bracketed corpora. the inside-outside algorithm for inferring the parameters of a stochastic context-free grammar is extended to take advantage of constituent information (constituent bracketing) in a partially parsed corpus. experiments on formal and natural language parsed corpora show that the new algorithm can achieve faster convergence and better modeling of hierarchical structure than the original one. in particular, over 90% test set bracketing accuracy was achieved for grammars inferred by our algorithm from a training set of handparsed part-of-speech strings for sentences in the air travel information system spoken language corpus. finally, the new algorithm has better time complexity than the original one when sufficient bracketing is provided.
an automatic method of finding topic boundaries. this article outlines a new method of locating discourse boundaries based on lexical cohesion and a graphical technique called dotplotting. the application of dotplotting to discourse segmentation can be performed either manually, by examining a graph, or automatically, using an optimization algorithm. the results of two experiments involving automatically locating boundaries between a series of concatenated documents are presented. areas of application and future directions for this work are also outlined.
distributional clustering of english words. we describe and evaluate experimentally a method for clustering words according to their distribution in particular syntactic contexts. words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the similarity measure for clustering. clusters are represented by average context distributions derived from the given words according to their probabilities of cluster membership. in many cases, the clusters can be thought of as encoding coarse sense distinctions. deterministic annealing is used to find lowest distortion sets of clusters: as the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical "soft" clustering of the data. clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.
statistical models for topic segmentation. most documents are about more than one subject, but many nlp and ir techniques implicitly assume documents have just one topic. we describe new clues that mark shifts to new topics, novel algorithms for identifying topic boundaries and the uses of such boundaries once identified. we report topic segmentation performance on several corpora as well as improvement on an ir task that benefits from good segmentation.