waashk/dblp huggingface dataset:
a computational model of language performance: data oriented parsing. data oriented parsing (dop) is a model where no abstract rules, but language experiences in the form of an analyzed corpus, constitute the basis for language processing. analyzing a new input means that the system attempts to find the most probable way to reconstruct the input out of fragments that already exist in the corpus. disambiguation occurs as a side-effect dop can be implemented by using conventional parsing strategies.
a machine translation system for foreign news in satellite broadcasting. a machine translation system of english to japanese is described, which has been used in 24-hour direct satellite broadcasting by nhk to translate "world news."in order to treat a wide scope of news sentences, the system is provided with more than 100,000 lexical entries as well as about 3,000 grammatical rules which can robustly analyze various types of undefined words. it is also effective in translation of news sentences to preprocess proper nouns, to resolve structural ambiguities by weighting grammatical rules, and to select appropriate words using semantic markers. the operational experiments on machine translation in satellite broadcasting are briefly discussed.
the e-framework: a formalism for natural language processing. this paper presents the most important characteristic of the new formalism used in eurotra, the e-framework. it is a formalism for natural language processing within a stratificational model.in the e-framework, mapping between levels of representation is performed on the basis of transitions between trees and partial descriptions of objects, called descriptors. these descriptors are completed using the definition of the target level.the tree to descriptor strategy simplifies the expression of complex two-way relations between text and abstract representations when major structural changes have to be performed on the input. this is illustrated by way of a detailed example showing the interaction of the two formal devices of the e-framework, the translator and the generator, the basic ideas of which are also briefly described.the e-framework has been implemented and forms the basis of the development of eurotra's pre-industrial prototype of a transfer-based, multi-lingual machine translation system.
using a natural-artificial hybrid language for database access. in this paper we propose a natural-artificial hybrid language for database access. the global construction of a sentence in this language is highly schematic, but allows expressions in the chosen language such as japanese or english. its artificial language part, sml, is closely related to our newly introduced data model, called scaled lattice. adopting japanese as its natural language part, we implemented a japanese-sml hybrid language processing system for our compact database system sclams, whose database consists of scaled lattices. the main features of this implementation are (1) a small lexicon and limited grammar, and (2) an almost free form in writing kana japanese.
a stochastic topological parser for german. we present a new approach to topological parsing of german which is corpus-based and built on a simple model of probabilistic cfg parsing. the topological field model of german provides a linguistically motivated, flat macro structure for complex sentences. besides the practical aspect of developing a robust and accurate topological parser for hybrid shallow and deep nlp, we investigate to what extent topological structures can be handled by context-free probabilistic models. we discuss experiments with systematic variants of a topological treebank grammar, which yield competitive results.
arabic finite-state morphological analysis and generation. this paper describes a large-scale system that performs morphological analysis and generation of on-line arabic words represented in the standard orthography, whether fully voweled, partially voweled or unvoweled. analyses display the root, pattern and all other affixes together with feature tags indicating part of speech, person, number, mood, voice, aspect, etc. the system is based on lexicons and rules from an earlier kimmo-style two-level morpholgical system, reworked extensively using xerox finite-state morphology tools. the results is an arabic finite-state lexical transducer that is applied with the same runtime code used for english, french, german, spanish, portuguese, dutch and italian lexical transducers.
methodology and verifiability in montague grammar. methodological problems in montague grammar are discussed. our observations show that a model-theoretic approach to natural language semantics in inadequate with respect to its verifiability from logical point of view. but, the formal attitudes seem to be of use for the development in computational linguistics.
periphrase: lingware for parsing and structural transfer. periphrase is a high-level computer language developed by a.l.p. systems to facilitate parsing and structural transfer. it is designed to speed the development of computer-assisted translation systems and grammar checkers. we describe the syntax and semantics of this tool, its integrated development environment, and some of our experience with it.
situational investigation of presupposition. this paper gives a formal theory of presupposition using situation semantics developed by barwise and perry. we will slightly modify barwise and perry's original theory of situation semantics so that we can deal with non-monatonic reasonings which are very important for the formalization of presupposition in natural language. this aspect is closely related to the formalization of incomplete knowledge in artificial intelligence.
knowledge representation method based on predicate calculus in an intelligent cai system. the knowledge representation method is introduced to be applied in the icai system to teach programming language. knowledge about syntax and semantios of that language is represented by a set of axioms written in the predicate calculus language. the directed graph of concepts is mentioned as a method to represent an instructional structure of the domain knowledge. the proof procedure to answer student's questions is described.
consequence relations in drt. we discuss some consequence relations in drt useful to discourse semantics. we incorporate some consequence relations into drt using sequent calculi. we also show some connections of these consequence relations and existing partial logics. our attempt enables us to display several versions of drt by employing different consequence relations.
feature graphs and abstract data types: a unifying approach. feature graphs appearing in unification-based grammar formalisms and algebraic specifications of abstract data types (adts) are both used for defining a collection of objects together with functions between these object sets. starting from this observation we define an algebraic semantics for feature graphs by assigning an algebraic specification to each feature graph. this opens the rich world of semantical foundations for abstract data types to the area of feature graphs and thus to unification grammar formalisms. using results from adt theory we define a simple and fast syntactic decision procedure testing the usual consistency, constant/complex consistency and acyclicity on the algebraic specification assigned to a feature graph. with this machinery at hand feature graph unification becomes union of feature graph specifications followed by the consistency test.
a bayesian approach for user modeling in dialogue systems. user modeling is an important components of dialog systems. most previous approaches are rule-based methods. in this paper, we propose to represent user models through bayesian networks. some advantages of the bayesian approach over the rule-based approach are as follows. first, rules for updating user models are not necessary because updating is directly performed by the evaluation of the network based on probability theory; this provides us a more formal way of dealing with uncertainties. second, the bayesian network provides more detailed information of users' knowledge, because the degree of belief on each concept is provided in terms of probability. we prove these advantages through a preliminary experiment.
learning grammars for different parsing tasks by partition search. this paper describes a comparative application of grammar learning by partition search to four different learning tasks: deep parsing, np identification, flat phrase chunking and np chunking. in the experiments, base grammars were extracted from a treebank corpus. from this starting point, new grammars optimised for the different parsing tasks were learnt by partition search. no lexical information was used. in half of the experiments, local structural context in the form of parent phrase category information was incorporated into the grammars. results show that grammars which contain this information outperform grammars which do not by large margins in all tests for all parsing tasks. it makes the biggest difference for deep parsing, typically corresponding to an improvement of around 5%. overall, partition search with parent phrase category information is shown to be a successful method for learning grammars optimised for a given parsing task, and for minimising grammar size. the biggest margin of improvement over a base grammar was a 5.4% increase in the f-score for deep parsing. the biggest size reductions were 93.5% fewer nonterminals (for np identification), and 31.3% fewer rules (for xp chunking)
using language and translation models to select the best among outputs from multiple mt systems. this paper addresses the problem of automatically selecting the best among outputs from multiple machine translation (mt) systems. existing approaches select the output assigned the highest score according to a target language model. in some cases, the existing approaches do not work well. this paper proposes two methods to improve performance. the first method is based on a multiple comparison test and checks whether a score from language and translation models is significantly higher than the others. the second method is based on probability that a translation is not inferior to the others, which is predicted from the above scores. experimental results show that the proposed methods achieve an improvement of 2 to 6% in performance.
weakly restricted stochastic grammars. a new type of stochastic grammars in introduced for investigation: weakly restricted stochastic grammars. in this paper we will concentrate on the consistency problem. to find conditions for stochastic grammars to be consistent, the theory of multitype galton-watson brancning processes and generating functions is of central importance. the unrestricted stochastic grammar formalism generates the same class of languages as the weakly restricted formalism. the inside-outside algorithm is adapted for use with weakly restricted grammars.
unsupervised learning of a rule-based spanish part of speech tagger. this paper describes a spanish part-of-speech (pos) tagger which applies and extends brill's algorithm for unsupervised learning of rule-based taggers (brill, 1995). first, we discuss our general approach including extensions we made to the algorithm in order to handle unknown words and parameterize learning and tagging options. next, we report and analyze our experimental results using different parameters. then, we describe our "hybrid" approach which was necessary in order to overcome a fundamental limitation in brill's original algorithm. finally, we compare our tagger with hidden markov model (hmm)-based taggers.
compilation of unification grammars with compositional semantics to speech recognition packages. in this paper a method to compile unification grammars into speech recognition packages is presented, and in particular, rules are specified to transfer the compositional semantics stated in unification grammars into speech recognition grammars. the resulting compiler creates a context-free backbone of the unification grammar, eliminates left-recursive productions and removes redundant grammar rules. the method was tested on a medium-sized unification grammar for english using nuance speech recognition software on a corpus of 131 utterances of 12 different speakers. results showed no significant computational overhead with respect to speech recognition performances for speech recognition grammar with compositional semantics compared to grammars without.
rule merging in a rule-based arabic stemmer. semitic languages require more complicated systems for processing their morphology. arabic language, for example, exhibits a very complex but very regular morphological structure. many approaches were proposed to analyze arabic language at the morphological level. proposed approaches can be classified into table lookup, linguistic, combinatorial and rule-based techniques.this paper proposes a new approach to enhance a rule-based arabic stemmer. the enhancement is based on rule merging process to reduce number of rules, increase language coverage and maintain the same level of performance.
presupposition & vp-ellipsis. we discuss a treatment of vp-ellipsis resolution in drt in general, and particularly cases where the source clause of the elliptical vp contains presupposition triggers. we propose to restrain vp-ellipsis resolution by presupposition neutralization. we view presupposition as a kind of anaphora, with the ability to accommodate an antecedent if not provided by discourse.
automatic semantic grouping in a spoken language user interface toolkit. with the rapid growth of real application domains for nlp systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific nlp systems. such a toolkit should provide an interface to accept sample sentences and convert them into semantic representations so as to allow programmers to map them to domain actions. in order to reduce the workload of managing a large number of semantic forms individually, the toolkit will perform what we call semantic grouping to organize the forms into meaningful groups. in this paper, we present three semantic grouping methods: similarity-based, verb-based and category-based grouping, and their implementation in the slui toolkit. we also discuss the pros and cons of each method and how they can be utilized according to the different domain needs.
an evaluation to detect and correct erroneous characters wrongly substituted, deleted and inserted in japanese and english sentences using markov models. in optical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. in order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a japanese "bunsetsu" and an english word, and to correct these errors, this paper proposes new methods using m-th order markov chain model for japanese "kanjikana" characters and english alphabets, assuming that markov probability of a correct chain of syllables or "kanji-kana" characters is greater than that of erroneous chains.from the results of the experiments, it is concluded that the methods is useful for detecting as well as correcting these errors in japanese "bunsetsu" and english words.
processing word order variation within a modified id/lp framework. from a well represented sample of world languages steele (1078) shows that about 70% of languages exhibit significant word order variation. only recently has this wide-spread phenomenon been drawing appropriate attention. perhaps id/lp (immediate dominance and linear precedence) framework is the most debated theories in this area. we point out some difficulties in processing standard id/lp grammar and present a modified version of the grammar. in the modified version, the right hand side of phrase structure rules is treated as a set or partially ordered set. an instance of the framework is implemented.
extending a broad-coverage parser for a general nlp toolkit. with the rapid growth of real world applications for nlp systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific nlp systems. such a toolkit should have a parser that is general enough to be used across domains, and yet accurate enough for each specific application. in this paper, we describe a parser that extends a broad-coverage parser, minipar (lin, 2001), with an adaptable shallow parser so as to achieve both generality and accuracy in handling domain specific nl problems. we test this parser on our corpus and the results show that the accuracy is significantly higher than a system that uses minipar alone.
some problems of machine translation between closely related languages. we describe the linguistic background of a czech-to-russian mt system, stressing its features resulting from the closed relatedness of the two languages, above all the possibility of a minimization of the transfer. related linguistic problems are analyzed within the mt project, as well as in the perspective of contrastive linguistics.
learning word clusters from data types. the paper illustrates a linguistic knowledge acquisition model making use of data types, infinite memory, and an inferential mechanism for inducing new information from known data. the model is compared with standard stochastic methods applied to data tokens, and tested on a task of lexico-semantic classification.
robust processing in machine translation. in this paper we provide an abstract characterisation of different kinds of robust processing in machine translation and natural language processing systems in terms of the kinds of problem they are supposed to solve. we focus on one problem which is typically exacerbated by robust processing, and for which we know of no existing solutions. we discuss two possible approaches to this, emphasising the need to correct or repair processing malfunctions.
an algorithm for identifying cognates between related languages. the algorithm takes as only input a list of words, preferably but not necessarily in phonemic transcription, in any two putatively related languages, and sorts it into decreasing order of probable cognation. the processing of a 250-item bilingual list takes about five seconds of cpu time on a dec kl1091, and requires 56 pages of core memory. the algorithm is given no information whatsoever about the phonemic transcription used, and even though cognate identification is carried out on the basis of a context-free one-for-one matching of individual characters, its cognation decisions are bettered by a trained linguist using more information only in cases of wordlists sharing less than 40% cognates and involving complex, multiple sound correspondences.
two methods for learning alt-j/e translation rules from examples and a semantic hierarchy. this paper presents our work towards the automatic acquisition of translation rules from japanese-english translation examples for ntt's alt-j/e machine translation system. we apply two machine learning algorithms: haussler's algorithm for learning internal disjunctive concept and quinlan's id3 algorithm. experimental results show that our approach yields rules that are highly accurate compared to the manually created rules.
combination of n-grams and stochastic context-free grammars for language modeling. this paper describes a hybrid proposal to combine n-grams and stochastic context-free grammars (scfgs) for language modeling. a classical n-gram model is used to capture the local relations between words, while a stochastic grammatical model is considered to represent the long-term relations between syntactical structures. in order to define this grammatical model, which will be used on large-vocabulary complex tasks, a category-based scfg and a probabilistic model of word distribution in the categories have been proposed. methods for learning these stochastic models for complex tasks are described, and algorithms for computing the word transition probabilities are also presented. finally, experiments using the penn treebank corpus improved by 30% the test set perplexity with regard to the classical n-gram models.
a model for transfer control in the metal mt-system. the present paper tries to outline a model to enhance the transfer control within the metal machine translation system. the model is being currently tested in the german-spanish system which is under development in barcelona and relies upon techniques belonging to the gpsg framework. the central idea is to extract from the transfer part of the phrase structure rules currently used by metal all the relevant generalizable information about feature traffic and control dependences, and put it in form of language-dependent tables. this information is then accessed and handled by a few high-level rule operators, called during the transfer process, implementing three general feature principles. the grammar writer is thereby relieved from the tedious task of controlling all the feature traffic between nodes, this resulting in a clearer, shorter and safer grammar for the system.
how the linguistic negation can have an effect in object-based knowledge representation model. in this paper, the coherence is considered within the framework of knowledge representation of texts. though the incoherence of a text may result from a lot of phenomena, we restrict ourselves in this communication to incoherence stemming from negations. we present the model and the effect of negation on its objects.
several directions for minority languages computerization. less than 1% of the languages spoken in the world are correctly "computerized": spell checkers, hyphenation, machine translation are still lacking for the others. in this paper, we present several directions that may help the computerization of minority languages as well as two projects where we apply some of these directions to the lao language.
computational semantics of time/negation interaction. the purpose of this paper is to study the interaction of time and negation in natural language, from a syntax/semantics interface point of view. this requires the definition of linguistically grounded semantic and syntactic representations. this is what we present in this paper; we show how the two representations we propose fit together in a computationally satisfying construction procedure.
un systeme inferentiel oriente objet pour des applications en langues naturelles. up to now, there is still no specific model for solving the problem of natural language representation and reasoning. in this paper, we propose an object oriented formalism for supporting knowledge representation, extraction and exploitation in the context of natural language processing.in the natural language analysis, this system is situated after the morpho-syntax and the linguistic semantics. it represents two classes of concepts: objects of discourse and action schemata, the former resulting from nominal syntagms and the latter from the 'processes'. we are concerned here just by the representation of objects.in the natural language discourse, manipulated objects are complex objects and the reasoning is by nature first inferential and then deductive. to take into account this kind of reasoning we need a suitable representation: a model of inferential objects.the theoretical foundations of the proposed model are lesniewski's logical systems: the calculus of names and the mereology. the former is based on a primitive functor called "epsilon" interpreted as is-a, the latter is based on a part-of relation which is called the "ingredience". the whole system is supported by these two primitives and theirs derived functions.the concepts of our model result from a collaboration between linguists and computer scientists. the main concepts are the intensional and extensional universes, notions and types.the possible inferential reasoning can be of different types: it can concern the status, the denominations, the structures or the "fonctifs" of the objects.