waashk/dblp huggingface dataset:
measuring language divergence by intra-lexical comparison. this paper presents a method for building genetic language taxonomies based on a new approach to comparing lexical forms. instead of comparing forms cross-linguistically, a matrix of language-internal similarities between forms is calculated. these matrices are then compared to give distances between languages. we argue that this coheres better with current thinking in linguistics and psycholinguistics. an implementation of this approach, called philologicon, is described, along with its application to dyen et al.'s (1992) ninety-five wordlists from indo-european languages.
spelling correction using context. this paper describes a spelling correction system that functions as part of an intelligent tutor that carries on a natural language dialogue with its users. the process that searches the lexicon is adaptive as is the system filter, to speed up the process. the basis of our approach is the interaction between the parser and the spelling corrector. alternative correction targets are fed back to the parser, which does a series of syntactic and semantic checks, based on the dialogue context, the sentence context, and the phrase context.
exploring and exploiting the limited utility of captions in recognizing intention in information graphics. this paper presents a corpus study that explores the extent to which captions contribute to recognizing the intended message of an information graphic. it then presents an implemented graphic interpretation system that takes into account a variety of communicative signals, and an evaluation study showing that evidence obtained from shallow processing of the graphic's caption has a significant impact on the system's success. this work is part of a larger project whose goal is to provide sight-impaired users with effective access to information graphics.
unification with lazy non-redundant copying. this paper presents a unification procedure which eliminates the redundant copying of structures by using a lazy incremental copying approach to achieve structure sharing. copying of structures accounts for a considerable amount of the total processing time. several methods have been proposed to minimize the amount of necessary copying. lazy incremental copying (lic) is presented as a new solution to the copying problem. it synthesizes ideas of lazy copying with the notion of chronological dereferencing for achieving a high amount of structure sharing.
ambiguity preserving machine translation using packed representations. in this paper we present an ambiguity preserving translation approach which transfers ambiguous lfg f-structure representatios. it is based on packed f-structure representations which are the result of potentially ambiguous utterances. if the ambiguities between source and target language can be preserved, no unpacking during transfer is necessary and the generator may produce utterances which maximally cover the underlying ambiguities. we convert the packed f-structure descriptions into a flat set of prolog terms which consist of predicates, their predicate argument structure and additional attribute-value information. ambiguity is expressed via local disjunctions. the flat representations facilitate the application of a shake-and-bake like transfer approach extended to deal with packed ambiguites.
handling linear precedence constraints by unification. linear precedence (lp) rules are widley used for stating word order principles. they have been adopted as constraints by hpsg but no encoding in the formalism has been provided. since they only order siblings, they are not quite adequate, at least not for german. we propose a notion of lp constraints that applies to linguistically motivated branching domains such as head domains. we show a type-based encoding in an hpsg-style formalism that supports processing. the encoding can be achieved by a compilation step.
minimizing manual annotation cost in supervised training from corpora. corpus-based methods for natural language processing often use supervised training, requiring expensive manual annotation of training corpora. this paper investigates methods for reducing annotation cost by sample selection. in this approach, during training the learning program examines many unlabeled examples and selects for labeling (annotation) only those that are most informative at each stage. this avoids redundantly annotating examples that contribute little new information. this paper extends our previous work on committee-based sample selection for probabilistic classifiers. we describe a family of methods for committee-based sample selection, and report experimental results for the task of stochastic part-of-speech tagging. we find that all variants achieve a significant reduction in annotation cost, though their computational efficiency differs. in particular, the simplest method, which has no parameters to tune, gives excellent results. we also show that sample selection yields a significant reduction in the size of the model used by the tagger.
towards a resource for lexical semantics: a large german corpus with extensive semantic annotation. we describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information, e.g. the construction of domain-independent lexica. the backbone of the annotation are semantic roles in the frame semantics paradigm. we report experiences and evaluate the annotated data from the first project stage. on this basis, we discuss the problems of vagueness and ambiguity in semantic annotation.
understanding natural language instructions: the case of purpose clauses. this paper presents an analysis of purpose clauses in the context of instruction understanding. such analysis shows that goals affect the interpretation and / or execution of actions, lends support to the proposal of using generation and enablement to model relations between actions, and sheds light on some inference processes necessary to interpret purpose clauses.
aggregation improves learning: experiments in natural language generation for intelligent tutoring systems. to improve the interaction between students and an intelligent tutoring system, we developed two natural language generators, that we systematically evaluated in a three way comparison that included the original system as well. we found that the generator which intuitively produces the best language does engender the most learning. specifically, it appears that functional aggregation is responsible for the improvement.
an empirical investigation of proposals in collaborative dialogues. we describe a corpus-based investigation of proposals in dialogue. first, we describe our dri compliant coding scheme and report our inter-coder reliability results. next, we test several hypotheses about what constitutes a well-formed proposal.
learning features that predict cue usage. our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. we apply a machine learning program, c4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.
approximating context-free grammars with a finite-state calculus. although adequate models of human language for syntactic analysis and semantic interpretation are of at least context-free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred. these requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing. a method is presented here for calculating such finite-state approximations from context-free grammars. it is essentially different from the algorithm introduced by pereira and wright (1991; 1996), is faster in some cases, and has the advantage of being open-ended and adaptable.
encoding lexicalized tree adjoining grammars with a nonmonotonic inheritance hierachy. this paper shows how datr, a widely used formal language for lexical knowledge representation, can be used to define an ltag lexicon as an inheritance hierarchy with internal lexical rules. a bottom-up featural encoding is used for ltag trees and this allows lexical rules to be implemented as covariation constraints within feature structures. such an approach eliminates the considerable redundancy otherwise associated with an ltag lexicon.
a structure-sharing parser for lexicalized grammars. in wide-coverage lexicalized grammars many of the elementary structures have substructures in common. this means that in conventional parsing algorithms some of the computation associated with different structures is duplicated. in this paper we describe a precompilation technique for such grammars which allows some of this computation to be shared. in our approach the elementary structures of the grammar are transformed into finite state automata which can be merged and minimised using standard algorithms, and then parsed using an automaton-based parser. we present algorithms for constructing automata from elementary structures, merging and minimising them, and string recognition and parse recovery with the resulting grammar.
noun-phrase analysis in unrestricted text for information retrieval. information retrieval is an important application area of natural-language processing where one encounters the genuine challenge of processing large quantities of unrestricted natural-language text. this paper reports on the application of a few simple, yet robust and efficient noun-phrase analysis techniques to create better indexing phrases for information retrieval. in particular, we describe an hybrid approach to the extraction of meaningful (continuous or discontinuous) subcompounds from complex noun phrases using both corpus statistics and linguistic heuristics. results of experiments show that indexing based on such extracted subcompound improves both recall and precision in an information retrieval system. the noun-phrase analysis techniques are also potentially useful for book indexing and automatic thesaurus extraction.
methods for the qualitative evaluation of lexical association measures. this paper presents methods for a qualitative, unbiased comparison of lexical association measures and the results we have obtained for adjective-noun pairs and preposition-noun-verb triples extracted from german corpora. in our approach, we compare the entire list of candidates, sorted according to the particular measures, to a reference set of manually identified "true positives". we also show how estimates for the very large number of hapaxlegomena and double occurrences can be inferred from random samples.
combining stochastic and rule-based methods for disambiguation in agglutinative languages. in this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to basque language1. the methods we have used in disambiguation are constraint grammar formalism and an hmm based tagger developed within the multext project. as basque is an agglutinative language, a morphological analyser is needed to attach all possible readings to each word. then, cg rules are applied using all the morphological features and this process decreases morphological ambiguity of texts. finally, we use the multext project tools to select just one from the possible remaining tags.using only the stochastic method the error rate is about 14%, but the accuracy may be increased by about 2% enriching the lexicon with the unknown words. when both methods are combined, the error rate of the whole process is 3.5%. considering that the training corpus is quite small, that the hmm model is a first order one and that constraint grammar of basque language is still in progress, we think that this combined method can achieve good results, and it would be appropriate for other agglutinative languages.
chinese-english term translation mining based on semantic prediction. using abundant web resources to mine chinese term translations can be applied in many fields such as reading/writing assistant, machine translation and cross-language information retrieval. in mining english translations of chinese terms, how to obtain effective web pages and evaluate translation candidates are two challenging issues. in this paper, the approach based on semantic prediction is first proposed to obtain effective web pages. the proposed method predicts possible english meanings according to each constituent unit of chinese term, and expands these english items using semantically relevant knowledge for searching. the refined related terms are extracted from top retrieved documents through feedback learning to construct a new query expansion for acquiring more effective web pages. for obtaining a correct translation list, a translation evaluation method in the weighted sum of multi-features is presented to rank these candidates estimated from effective web pages. experimental results demonstrate that the proposed method has good performance in chinese-english term translation acquisition, and achieves 82.9% accuracy.
optimizing story link detection is not equivalent to optimizing new event detection. link detection has been regarded as a core technology for the topic detection and tracking tasks of new event detection. in this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems. motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists. experimental results validate our hypothesis.
highly constrained unification grammars. unification grammars are widely accepted as an expressive means for describing the structure of natural languages. in general, the recognition problem is undecidable for unification grammars. even with restricted variants of the formalism, off-line parsable grammars, the problem is computationally hard. we present two natural constraints on unification grammars which limit their expressivity and allow for efficient processing. we first show that non-reentrant unification grammars generate exactly the class of context-free languages. we then relax the constraint and show that one-reentrant unification grammars generate exactly the class of mildly context-sensitive languages. we thus relate the commonly used and linguistically motivated formalism of unification grammars to more restricted, computationally tractable classes of languages.
anaphor resolution in unrestricted texts with partial parsing. in this paper we deal with several kinds of anaphora in unrestricted texts. these kinds of anaphora are pronominal references, surfacecount anaphora and one-anaphora. in order to solve these anaphors we work on the output of a part-of-speech tagger, on which we automatically apply a partial parsing from the formalism: slot unification grammar, which has been implemented in prolog. we only use the following kinds of information: lexical (the lemma of each word), morphologic (person, number, gender) and syntactic. finally we show the experimental results, and the restrictions and preferences that we have used for anaphor resolution with partial parsing.
using textual clues to improve metaphor processing. in this paper, we propose a textual clue approach to help metaphor detection, in order to improve the semantic processing of this figure. the previous works in the domain studied the semantic regularities only, overlooking an obvious set of regularities. a corpus-based analysis shows the existence of surface regularities related to metaphors. these clues can be characterized by syntactic structures and lexical markers. we present an object oriented model for representing the textual clues that were found. this representation is designed to help the choice of a semantic processing, in terms of possible non-literal meanings. a prototype implementing this model is currently under development, within an incremental approach allowing step-by-step evaluations.
how to thematically segemt texts by using lexical cohesion? this article outlines a quantitative method for segmenting texts into thematically coherent units. this method relies on a network of lexical collocations to compute the thematic coherence of the different parts of a text from the lexical cohesiveness of their words. we also present the results of an experiment about locating boundaries between a series of concatened texts.
thematic segmentation of texts: two methods for two kinds of text. to segment texts in thematic units, we present here how a basic principle relying on word distribution can be applied on different kind of texts. we start from an existing method well adapted for scientific texts, and we propose its adaptation to other kinds of texts by using semantic links between words. these relations are found in a lexical network, automatically built from a large corpus. we will compare their results and give criteria to choose the more suitable method according to text characteristics.
enhancing electronic dictionaries with an index based on associations. a good dictionary contains not only many entries and a lot of information concerning each one of them, but also adequate means to reveal the stored information. information access depends crucially on the quality of the index. we will present here some ideas of how a dictionary could be enhanced to support a speaker/writer to find the word s/he is looking for. to this end we suggest to add to an existing electronic resource an index based on the notion of association. we will also present preliminary work of how a subset of such associations, for example, topical associations, can be acquired by filtering a network of lexical co-occurrences extracted from a corpus.
a dynamic bayesian framework to model context and memory in edit distance learning: an application to pronunciation classification. sitting at the intersection between statistics and machine learning, dynamic bayesian networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. while natural language processing increasingly relies on statistical methods, we think they have yet to use graphical models to their full potential. in this paper, we report on experiments in learning edit distance costs using dynamic bayesian networks and present results on a pronunciation classification task. by exploiting the ability within the dbn framework to rapidly explore a large model space, we obtain a 40% reduction in error rate compared to a previous transducer-based method of learning edit distance.
automatic creation of domain templates. recently, many natural language processing (nlp) applications have improved the quality of their output by using various machine learning techniques to mine information extraction (ie) patterns for capturing information from the input text. currently, to mine ie patterns one should know in advance the type of the information that should be captured by these patterns. in this work we propose a novel methodology for corpus analysis based on cross-examination of several document collections representing different instances of the same domain. we show that this methodology can be used for automatic domain template creation. as the problem of automatic domain template creation is rather new, there is no well-defined procedure for the evaluation of the domain template quality. thus, we propose a methodology for identifying what information should be present in the template. using this information we evaluate the automatically created domain templates through the text snippets retrieved according to the created templates.
using lexical dependency and ontological knowledge to improve a detailed syntactic and semantic tagger of english. this paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics. our findings show that information from both these sources can lead to strong improvements in overall system accuracy: dependency knowledge improved performance over all classes of word, and knowledge of the position of a word in an on-tological hierarchy increased accuracy for words not seen in the training data. the resulting tagger offers the highest reported tagging accuracy on this tagset to date.
incorporating non-local information into information extraction systems by gibbs sampling. most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. we show how to solve this dilemma with gibbs sampling, a simple monte carlo method used to perform approximate inference in factored probabilistic models. by using simulated annealing in place of viterbi decoding in sequence models such as hmms, cmms, and crfs, it is possible to incorporate non-local structure while preserving tractable inference. we use this technique to augment an existing crf-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. this technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.
a layered approach to nlp-based information retrieval. a layered approach to information retrieval permits the inclusion of multiple search engines as well as multiple databases, with a natural language layer to convert english queries for use by the various search engines. the nlp layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on word-net.
offline strategies for online question answering: answering questions before they are asked. recent work in question answering has focused on web-based systems that extract answers using simple lexico-syntactic patterns. we present an alternative strategy in which patterns are used to extract highly precise relational information offline, creating a data repository that is used to efficiently answer questions. we evaluate our strategy on a challenging subset of questions, i.e. "who is ..." questions, against a state of the art web-based question answering system. results indicate that the extracted relations answer 25% more questions correctly and do so three orders of magnitude faster than the state of the art system.
structure-sharing in lexical representation. the lexicon now plays a central role in our implementation of a head-driven phrase structure grammar (hpsg), given the massive relocation into the lexicon of linguistic information that was carried by the phrase structure rules in the old gpsg system. hpsg's grammar contains fewer than twenty (very general) rules; its predecessor required over 350 to achieve roughly the same coverage. this simplification of the grammar is made possible by an enrichment of the structure and content of lexical entries, using both inheritance mechanisms and lexical rules to represent the linguistic information in a general and efficient form. we will argue that our mechanisms for structure-sharing not only provide the ability to express important linguistic generalization about the lexicon, but also make possible an efficient, readily modifiable implementation that we find quite adequate for continuing development of a large natural language system.