waashk/dblp huggingface dataset:
a quantitative evaluation of linguistic tests for the automatic prediction of semantic markedness. we present a corpus-based study of methods that have been proposed in the linguistics literature for selecting the semantically unmarked term out of a pair of antonymous adjectives. solutions to this problem are applicable to the more general task of selecting the positive term from the pair. using automatically collected data, the accuracy and applicability of each method is quantified, and a statistical analysis of the significance of the results is performed. we show that some simple methods are indeed good indicators for the answer to the problem while other proposed methods fail to perform better than would be attributable to chance. in addition, one of the simplest methods, text frequency, dominates all others. we also apply two generic statistical learning methods for combining the indications of the individual methods, and compare their performance to the simple methods. the most sophisticated complex learning method offers a small, but statistically significant, improvement over the original tests.
predicting the semantic orientation of adjectives. we identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. a log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus.
expanding the horizons of natural language interfaces. current natural language interfaces have concentrated largely on determining the literal "meaning" of input from their users. while such decoding is an essential underpinning, much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication, such as robust communication procedures.this paper defends that view. but claims that direct imitation of human performance is not the best way to implement many of these non-literal aspects of communication; that the new technology of powerful personal computers with integral graphics displays offers techniques superior to those of humans for these aspects, while still satisfying human communication needs. the paper proposes interfaces based on a judicious mixture of these techniques and the still valuable methods of more traditional natural language interfaces.
flexible parsing. when people use natural language in natural settings, they often use it ungrammatically, leaving out or repeating words, breaking off and restarting, speaking in fragments, etc. their human listeners are usually able to cope with these deviations with little difficulty. if a computer system is to accept natural language input from its users on a routine basis, it should be similarly robust. in this paper, we outline a set of parsing flexibilities that such a system should provide. we go on to describe flexp, a bottom-up pattern matching parser that we have designed and implemented to provide many of these flexibilities for restricted natural language input to a limited-domain computer system.
metaphoric generalization through sort coercion. this paper presents a method for interpreting metaphoric language in the context of a portable natural language inferface. the method licenses metaphoric uses via coercions between incompatible ontological sorts. the machinery allows both previously-known and unexpected metaphoric uses ot be correctly interpreted and evaluated with respect to the backend expert system.
multi-paragraph segmentation of expository text. this paper describes texttiling, an algorithm for partitioning expository texts into coherent multi-paragraph discourse units which reflect the subtopic structure of the texts. the algorithm uses domain-independent lexical frequency and distribution information to recognize the interactions of multiple simultaneous themes. two fully-implemented versions of the algorithm are described and shown to produce segmentation that corresponds well to human judgments of the major subtopic boundaries of thirteen lengthy texts.
untangling text data mining. the possibilities for data mining from large text collections are virtually untapped. text expresses a vast, rich range of information, but encodes this information in a form that is difficult to decipher automatically. perhaps for this reason, there has been little work in text data mining to date, and most people who have talked about it have either conflated it with information access or have not made use of text directly to discover heretofore unknown information. in this paper i will first define data mining, information access, and corpus-based computational linguistics, and then discuss the relationship of these to text data mining. the intent behind these contrasts is to draw attention to exciting new kinds of problems for computational linguists. i describe examples of what i consider to be real text data mining efforts and briefly outline recent ideas about how to pursue exploratory data analysis over text.
collaborating on referring expressions. this paper presents a computational model of how conversational participants collaborate in making referring expressions. the model is based on the planning paradigm. it employs plans for constructing and recognizing referring expressions and meta-plans for constructing and recognizing clarifications. this allows the model to account for the generation and understanding both of referring expressions and of their clarifications in a uniform framework using a single knowledge base.
deyecting and correcting speech repairs. interactive spoken dialog provides many new challenges for spoken language systems. one of the most critical is the prevalence of speech repairs. this paper presents an algorithm that detects and corrects speech repairs based on finding the repair pattern. the repair pattern is built by finding word matches and word replacements, and identifying fragments and editing terms. rather than using a set of prebuilt templates, we build the pattern on the fly. in a the fair test, our method, when combined with a statistical model to filter possible repairs, was successful at detecting and correcting 80% of the repairs, without using prosodic information or a parser.
intonational boundaries, speech repairs and discourse markers: modeling spoken dialog. to understand a speaker's turn of a conversation, one needs to segment it into intonational phrases, clean up any speech repairs that might have occurred, and identify discourse markers. in this paper, we argue that these problems must be resolved together, and that they must be resolved early in the processing stream. we put forward a statistical language model that resolves these problem, does pos tagging, and can be used as the language model of a speech recognizer. we find that by accounting for the interactions between these tasks that the performance on each task improves, as does pos tagging and perplexity.
definiteness predictions for japanese noun phrases. one of the major problems when translating from japanese into a eurpoean language such as german or english is to determine definiteness of noun phrases in order to choose the correct determiner in the target language. even though in japanese, noun phrase reference is said to depend in large parts on the discourse context, we show that in many cases there also exist linguistic markers for definiteness. we use these to build a rule hierarchy that predicts 79,5% of the articles with an accuracy of 98,9% from syntactic-semantic properties alone, yielding an efficient pre-processing tool for the computationally expensive context checking.
eliminative parsing with graded constraints. natural language parsing is conceived to be a procedure of disambiguation, which successively reduces an initially totally ambiguous structural representation towards a single interpretation. graded constraints are used as means to express well-formedness conditions of different strength and to decide which partial structures are locally least preferred and, hence, can be deleted. this approach facilitates a higher degree of robustness of the analysis, allows to introduce resource adaptivity into the parsing procedure, and exhibits a high potential for parallelization of the computation.
optimal multi-paragraph text segmentation by dynamic programming. there exist several methods of calculating a similarity curve, or a sequence of similarity values, representing the lexical cohesion of successive text constituents, e.g., paragraphs. methods for deciding the locations of fragment boundaries are, however, scarce. we propose a fragmentation method based on dynamic programming. the method is theoretically sound and guaranteed to provide an optimal splitting on the basis of a similarity curve, a preferred fragment length, and a cost function defined. the method is especially useful when control on fragment size is of importance.
discriminative training of a neural network statistical parser. discriminative methods have shown significant improvements over traditional generative methods in many machine learning applications, but there has been difficulty in extending them to natural language parsing. one problem is that much of the work on discriminative methods conflates changes to the learning method with changes to the parameterization of the problem. we show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model. we present three methods for training a neural network to estimate the probabilities for a statistical parser, one generative, one discriminative, and one where the probability model is generative but the training criteria is discriminative. the latter model outperforms the previous two, achieving state-of-the-art levels of performance (90.1% f-measure on constituents).
a connectionist parser for structure unification grammar. this paper presents a connectionist syntactic parser which uses structure unification grammar as its grammatical framework. the parser is implemented in a connectionist architecture which stores and dynamically manipulates symbolic representations, but which can't represent arbitrary disjunction and has bounded memory. these problems can be overcome with structure unification grammar's extensive use of partial descriptions.
a connectionist architecture for learning to parse. we present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. the architecture can represent syntactic constituents, and can learn generalizations over syntactic constituents, thereby addressing the sparse data problems of previous connectionist architectures. we apply these simple synchrony networks to mapping sequences of word tags to parse trees. after training on parsed samples of the brown corpus, the networks achieve precision and recall on constituents that approaches that of statistical methods for this task.
data-defined kernels for parse reranking derived from probabilistic models. previous research applying kernel methods to natural language parsing have focussed on proposing kernels over parse trees, which are hand-crafted based on domain knowledge and computational considerations. in this paper we propose a method for defining kernels in terms of a probabilistic model of parsing. this model is then trained, so that the parameters of the probabilistic model reflect the generalizations in the training data. the method we propose then uses these trained parameters to define a kernel for reranking parse trees. in experiments, we use a neural network based statistical parser as the probabilistic model, and use the resulting kernel with the voted perceptron algorithm to rerank the top 20 parses from the probabilistic model. this method achieves a significant improvement over the accuracy of the probabilistic model.
independence and commitment: assumptions for rapid training and execution of rule-based pos taggers. this paper addresses the rule-based pos tagging method of brill, and questions the importance of rule interactions to its performance. adopting two assumptions that serve to exclude rule interactions during tagging and training, we arrive at some variants of brill's approach that are instances of decision list models. these models allow for both rapid training on large data sets and rapid tagger execution, giving tagging accuracy that is comparable to, or better than the brill method.
efficient incremental processing with categorial grammar. some problems are discussed that arise for incremental processing using certain flexible categorial grammars, which involve either undesirable parsing properties or failure to allow combinations useful to incrementality. we suggest a new calculus which, though 'designed' in relation to categorial interpretations of some notions of dependency grammar, seems to provide a degree of flexibility that is highly appropriate for incremental interpretation. we demonstrate how this grammar may be used for efficient incremental parsing, by employing normalisation techniques.
maximal incrementality in linear categorial deduction. recent work has seen the emergence of a common framework for parsing categorial grammar (cg) formalisms that fall within the 'type-logical' tradition (such as the lambek calculus and related systems), whereby some method of linear logic theorem proving is used in combination with a system of labelling that ensures only deductions appropriate to the relevant grammatical logic are allowed. the approaches realising this framework, however, have not so far addressed the task of incremental parsing --- a key issue in earlier work with 'flexible' categorial grammars. in this paper, the approach of (hepple, 1996) is modified to yield a linear deduction system that does allow flexible deduction and hence incremental processing, but that hence also suffers the problem of 'spurious ambiguity'. this problem is avoided via normalisation.
memoisation for glue language deduction and categorial parsing. the multiplicative fragment of linear logic has found a number of applications in computational linguistics: in the "glue language" approach to lfg semantics, and in the formulation and parsing of various categorial grammars. these applications call for efficient deduction methods. although a number of deduction methods for multiplicative linear logic are known, none of them are tabular methods, which bring a substantial efficiency gain by avoiding redundant computation (c.f. chart methods in cfg parsing): this paper presents such a method, and discusses its use in relation to the above applications.
an earley-style predictive chart parsing method for lambek grammars. we present a new chart parsing method for lambek grammars, inspired by a method for d-tree grammar parsing. the formulae of a lambek sequent are firstly converted into rules of an indexed grammar formalism, which are used in an earley-style predictive chart algorithm. the method is non-polynomial, but performs well for practical purposes---much better than previous chart methods for lambek grammars.
learning parse and translation decisions from examples with rich context. we present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the wall street journal. applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. it relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.
on the spatial uses of prepositions. at first glance, the spatial uses of prepositions seem to constitute a good semantic domain for a computational approach. one expects such uses will refer more or less strictly to a closed, explicit, and precise chunk of world knowledge. such an attitude is expressed in the following statement:"given descriptions of the shape of two objects, given their location (for example, by means or coordinates in some system of reference), and, in some cases, the location of an observer, one can select an appropriate preposition."this paper shows the fallacy of this claim. it addresses the problem of interpreting and generating "locative predications" (expressions made up of two noun-phrases governed by a preposition used spatially). it identifies and describes a number of object characteristics beyond shape (section 1) and contextual factors (section 2) which bear on these processes. drawing on these descriptions, the third section proposes core meanings for two categories of prepositions, and describes some of the transformations these core meanings are subject to in context. the last section outlines the main directions of inquiry suggested by the examples and observations in the paper.
ferret: interactive question-answering for real-world environments. this paper describes ferret, an interactive question-answering (q/a) system designed to address the challenges of integrating automatic q/a applications into real-world environments. ferret utilizes a novel approach to q/a - known as predictive questioning - which attempts to identify the questions (and answers) that users need by analyzing how a user interacts with a system while gathering information related to a particular scenario.
corpus-based discourse understanding in spoken dialogue systems. this paper concerns the discourse understanding process in spoken dialogue systems. this process enables the system to understand user utterances based on the context of a dialogue. since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understanding result after each user utterance. by holding multiple candidates for understanding results and resolving the ambiguity as the dialogue progresses, the discourse understanding accuracy can be improved. this paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora. unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process. experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective.
learning to generate naturalistic utterances using reviews in spoken dialogue systems. spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of concepts the system wants to express and realizations of those concepts. dictionary creation is a costly process; it is currently done by hand for each dialogue domain. we propose a novel unsupervised method for learning such mappings from user reviews in the target domain, and test it on restaurant reviews. we test the hypothesis that user reviews that provide individual ratings for distinguished attributes of the domain entity make it possible to map review sentences to their semantic representation with high precision. experimental analyses show that the mappings learned cover most of the domain ontology, and provide good linguistic variation. a subjective user evaluation shows that the consistency between the semantic representations and the learned realizations is high and that the naturalness of the realizations is higher than a hand-crafted baseline.
parsing parallel grammatical representation. traditional accounts of quantifier scope employ qualitative constraints or rules to account for scoping preferences. this paper outlines a feature-based parsing algorithm for a grammar with multiple simultaneous levels of representation, one of which corresponds to a partial ordering among quantifiers according to scope. the optimal such ordering (as well as the ranking of other orderings) is determined in this grammar not by absolute constraints, but by stochastic heuristics based on the degree of alignment among the representational levels. a prolog implementation is described and its accuracy is compared with that of other accounts.
acquiring disambiguation rules from text. an effective procedure for automatically acquiring a new set of disambiguation rules for an existing deterministic parser on the basis of tagged text is presented. performance of the automatically acquired rules is much better than the existing hand-written disambiguation rules. the success of the acquired rules depends on using the linguistic information encoded in the parser; enhancements to various components of the parser improves the acquired rule set. this work suggests a path toward more robust and comprehensive syntactic analyzers.
noun classification from predicate-argument structures. a method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. the resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification.
structural ambiguity and lexical relations. we propose that many ambiguous prepositional phrase attachments can be resolved on the basis of the relative strength of association of the preposition with verbal and nominal heads, estimated on the basis of distribution in an automatically parsed corpus. this suggests that a distributional approach can provide an approximate solution to parsing problems that, in the worst case, call for complex reasoning.
two constraints on speech act ambiguity. existing plan-based theories of speech act interpretation do not account for the conventional aspect of speech acts. we use patterns of linguistic features (e.g. mood, verb form, sentence adverbials, thematic roles) to suggest a range of speech act interpretations for the utterance. these are filtered using plan-based conversational implicatures to eliminate inappropriate ones. extended plan reasoning is available but not necessary for familiar forms. taking speech act ambiguity seriously, with these two constraints, explains how "can you pass the salt?" is a typical indirect request while "are you able to pass the salt?" is not.
graph branch algorithm: an optimum tree search method for scored dependency graph with arc co-occurrence constraints. various kinds of scored dependency graphs are proposed as packed shared data structures in combination with optimum dependency tree search algorithms. this paper classifies the scored dependency graphs and discusses the specific features of the "dependency forest" (df) which is the packed shared data structure adopted in the "preference dependency grammar" (pdg), and proposes the "graph branch algorithm" for computing the optimum dependency tree from a df. this paper also reports the experiment showing the computational amount and behavior of the graph branch algorithm.
a prosodic analysis of discourse segments in direction-giving monologues. this paper reports on corpus-based research into the relationship between intonational variation and discourse structure. we examine the effects of speaking style (read versus spontaneous) and of discourse segmentation method (text-alone versus text-and-speech) on the nature of this relationship. we also compare the acoustic-prosodic features of initial, medial, and final utterances in a discourse segment.