waashk/dblp huggingface dataset:
a production system model of first language acquisition. amber is a model of first language acquisition that improves its performance through a process of error recovery. the model is implemented in actg, an adaptive production system language. amber starts with the ability to say only one word at a time, but adds rules for inserting additional words in the correct order, based on comparisons between predicted and observed sentences. these insertion rules may be overly general and lead to errors of commission; in turn, these lead to more conservative rules with additional conditions. amber's learning mechanisms account for many of the developments observed in children's speech.
a generalized reconstruction algorithm for ellipsis resolution. we present an algorithm which assigns interpretations to several major types of ollipsis structures through a generalized procedure of syntactic reconstruction. ellipsis structures are taken to be sequences of lexically realized arguments and/or adjuncts of an empty verbal head. reconstruction is characterized as the specification of a (partial) correspondence relation between the unrealized head verb of an elided clause and its argument and adjuncts on one hand, and the head of a non-elided antecedent sentence and its arguments and adjuncts on the other. the algorithm generates appropriate interpretations for cases of vp ellipsis, pseudo-gapping, bare ellipsis (stripping), and gapping. it provides a uniform computational approach to a wide range of ellipsis phenomena, and it has significant advantages over several other approaches to ellipsis which have recently been suggested in the computational and linguistic literature.
active schemata and their role in semantic parsing. in the past years we have been applying semantic atn-grammars - as introduced by brown & burton (1974) - to natural language question-answering tasks (e.g. a lisp-tutor [barth, 1977], a question-answering system about the micro-world of soccer [rathke & sonntag, 1979]). we found that semantic grammars execute efficiently, but become large very quickly even with moderate domains of discourse. we therefore looked for ways to support parsing by domain-dependent knowledge represented in an inheritance network [laubsch, 1979]. in this paper we first briefly describe our representation language objtalk, and then illustrate how it is used for building an understanding system for processing german newspaper texts about the jobmarket situation.
language generation from conceptual structure: synthesis of german in a japanese/german mt project. this paper describes the current state of the semsyn project, whose goal is to develop a module for generation of german from a semantic representation. the first application of this module is within the framework of a japanese/german machine translation project. the generation process is organized into three stages that use distinct knowledge sources. the first stage is conceptually oriented and language independent, and exploits case and concept schemata. the second stage employs realization schemata which specify choices to map from meaning structures into german linguistic constructs. the last stage constructs the surface string using knowledge about syntax, morphology, and style. this paper describes the first two stages.
machine translation : what type of post-editing on what type of documents for what type of users. various typologies of technical and scientifical texts have already been proposed by authors involved in multilingual transfer problems. they were usually aimed at a better knowledge of the criteria for deciding if a document has to be or can be machine translated. such a typology could also lead to a better knowledge of the typical errors occuring, and so lead to more appropriate post-editing, as well as to improvements in the system.raw translations being usable, as they are quite often for rapid information needs, it is important to draw the limits between a style adequate for rapid information, and an elegant, high quality style such as required for information large dissemination. style could be given a new definition through a linguistic analysis based on machine translation, on communication situations and on the users' requirements and satisfaction.
when something is missing: ellipsis, coordination and the chart. this paper deals with two linguistic phenomena which are usually considered cases of ill-formedness by the computational linguistics community: intersentential ellipsis and coordination (possibly with gaps). we present an original solution, if compared to those already known for the two phenomena. this solution is conceived within a relevant approach to parsing, i.e. chart parsing, and is coherent with the basic ideas of this approach.
multi-lingual translation of spontaneously spoken language in a limited domain. janus is a multi-lingual speech-to-speech translation system designed to facilitate communication between two parties engaged in a spontaneous conversation in a limited domain. in an attempt to achieve both robustness and translation accuracy we use two different translation components: the glr module, designed to be more accurate, and the phoenix module, designed to be more robust. we analyze the strengths and weaknesses of each of the approaches and describe our work on combining them. another recent focus has been on developing a detailed end-to-end evaluation procedure to measure the performance and effectiveness of the system. we present our most recent spanish-to-english performance evaluation results.
n-th order ergodic multigram hmm for modeling of languages without marked word boundaries. ergodic hmms have been successfully used for modeling sentence production. however for some oriental languages such as chinese, a word can consist of multiple characters without word boundary markers between adjacent words in a sentence. this makes word-segmentation on the training and testing data necessary before ergodic hmm can be applied as the language model. this paper introduces the n-th order ergodic multigram hmm for language modeling of such languages. each state of the hmm can generate a variable number of characters corresponding to one word. the model can be trained without word-segmented and tagged corpus, and both segmentation and tagging are trained in one single model. results on its application on a chinese corpus are reported.
proof-nets and dependencies. proof-nets (roorda 1990) are a good device for processing with categorial grammars, mainly because they avoid spurious ambiguities. nevertheless, they do not provide easily readable structures and they hide the true proximity between categorial grammars and dependency grammars. we give here an other kind of proof-nets which is much related to dependency structures similar to those we meet in, for instance (hudson 1984). these new proof-nets are called connection nets. we show that connection nets provide not only easily interpretable structures, but also that processing with them is more efficient.
contextual natural language processing and daml for understanding software requirements specifications. in software engineering a system requirements document written in a natural language (nl) needs to be translated into one of the formal specification languages for system execution. when this translation is to be automated, resolution of the ambiguity in the document and explicit definition of implicit domain knowledge are necessary. in our approach, contextual natural language processing is used to overcome the ambiguity and the domain knowledge is expressed in darpa agent markup language (daml). the result is a formal representation of the informal requirements in nl for prototyping and even for implementation.
the grammatical function analysis between korean adnoun clause and noun phrase by using support vector machines. this study aims to improve the performance of identifying grammatical functions between an adnoun clause and a noun phrase in korean. the key task is to determine the relation between the two constituents in terms of such functional categories as subject, object, adverbial, and appositive. the problem is mainly caused by the fact that functional morphemes, which are considered to be crucial for identifying the relation, are frequently omitted in the noun phrases. to tackle this problem, we propose to employ the support vector machines(svm) in determining the grammatical functions. through an experiment with a tagged corpus for training svms, the proposed model is found to be useful.
translation selection through source word sense disambiguation and target word selection. a word has many senses, and each sense can be mapped into many target words. therefore, to select the appropriate translation with a correct sense, the sense of a source word should be disambiguated before selecting a target word. based on this observation, we propose a hybrid method for translation selection that combines disambiguation of a source word sense and selection of a target word. knowledge for translation selection is extracted from a bilingual dictionary and target language corpora. dividing translation selection into the two sub-problems, we can make knowledge acquisition straight-forward and select more appropriate target words.
implicit ambiguity resolution using incremental clustering in korean-to-english cross-language information retrieval. this paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in korean-to-english cross-language information retrieval. in the framework we propose, a query in korean is first translated into english by looking up korean-english dictionary, then documents are retrieved based on the vector space retrieval for the translated query terms. for the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using clusters. in experiment on trec-6 clir test collection, our method achieved 28.29% performance improvement for translated queries without ambiguity resolution for queries. this corresponds to 97.27% of the monolingual performance for original queries. when we combine our method with query ambiguity resolution, our method even outperforms the monolingual retrieval.
table-driven neural syntactic analysis of spoken korean. a cyk-table-driven interactive relaxation parsing method of spoken korean, integrated with the cyk-based morphological analysis is introduced. an extension of the categorial grammar is introduced to treat the free wordorder in korean. the table-driven control of interactive relaxation gives efficiency in constituent searching and expectation generation. the lexical nature of the categorial grammar and the distributed nature of the interactive relaxation parsing together show a smooth integration of both bottom-up and top-down effects during the spoken language analysis.
natural language interpretations for heterogeneous database access. in order to query into diverse types of databases and to integrate the resulting information, dispersed throughout the network in a specific domain, we must address complex problems due primarily to heterogeneity of the involved databases. in this paper, we propose to model access to heterogeneous databases, by interpreting natural language queries into queries in formal languages such as sql, oql, and cpl by accounting for various language-specific constructions including join relations, path expressions, and object bindings with domain resources and a common lexicon, in a combinatory categorial grammar framework.
lexicalized hidden markov models for part-of-speech tagging. since most previous works for hmm-based tagging consider only part-of-speech information in contexts, their models cannot utilize lexical information which is crucial for resolving some morphological ambiguity. in this paper we introduce uniformly lexicalized hmms for part-of-speech tagging in both english and korean. the lexicalized models use a simplified back-off smoothing technique to overcome data sparseness. in experiments, lexicalized models achieve higher accuracy than non-lexicalized models and the back-off smoothing method mitigates data sparseness better than simple smoothing methods.
an ascription-based approach to speech acts. the two principal areas of natural language processing research in pragmantics are belief modelling and speech act processing. belief modelling is the development of techniques to represent the mental attitudes of a dialogue participant. the latter approach, speech act processing, based on speech act theory, involves viewing dialogue in planning terms. utterances in a dialogue are modelled as steps in a plan where understanding an utterance involves deriving the complete plan a speaker is attempting to achieve. however, previous speech act based approaches have been limited by a reliance upon relatively simplistic belief modelling techniques and their relationship to planning and plan recognition. in particular, such techniques assume precomputed nested belief structures. in this paper, we will present an approach to speech act processing based on novel belief modelling techniques where nested beliefs are propagated on demand.
automatic model refinement - with an application to tagging. statistical nlp models usually only consider coarse information and very restricted context to make the estimation of parameters feasible. to reduce the modeling error introduced by a simplified probabilistic model, the classification and regression tree (cart) method was adopted in this paper to select more discriminative features for automatic model refinement. because the features are adopted dependently during splitting the classification tree in cart, the number of training data in each terminal node is small, which makes the labeling process of terminal nodes not robust. this over-tuning phenomenon cannot be completely removed by cross - validation process (i.e., pruning process). a probabilistic classification model based on the selected discriminative features is thus proposed to use the training data more efficiently. in tagging the brown corpus, our probabilistic classification model reduces the error rate of the top 10 error dominant words from 5.71% to 4.35%, which shows 23.82% improvement over the unrefined model.
principar - an efficient, broad-coverage, principle-based parser. we present an efficient, broad-coverage, principle-based parser for english. the parser has been implemented in c++ and runs on sun sparcstations with x-windows. it contains a lexicon with over 90,000 entries, constructed automatically by applying a set of extraction and conversion rules to entries from machine readable dictionaries.
interpreting syntactically ill-formed sentences. the paper discusses three different kinds of syntactic ill-formedness: ellipsis, conjunctions, and actual syntactic errors. it is shown how a new grammatical formalism, based on a two-level representation of the syntactic knowledge is used to cope with ill-formed sentences. the basic control structure of the parser is briefly sketched; the paper shows that it can be applied without any substantial change both to correct and to ill-formed sentences. this is achieved by introducing a mechanism for the hypothesization of syntactic structures, which is largely independent of the rules defining the well-formedness. on the contrary, the second level of syntactic knowledge embodies those rules and is used to validate the hypotheses emitted by the first level. alternative hypotheses are obtained, when needed, by means of local reorganizations of the parse tree. sentence fragments are handled by the same mechanism, but in this case the second level rules are used to detect the absence of one (or more) constituents.
interpretation of noun phrases in intensional contexts. this paper presents a network formalism for representing the meaning of noun phrases occurring in the context of intensional verbs such as seek and want. the basic assumption is that an intermediate representation is useful to carry out the interpretation process perspicuously. the proposed representation keeps apart de-re (transparent) and de-dicto readings, stating, by means of suitable arcs and nodes, that in the first case there is a real counterpart of the described entity, whereas in the second case no such counterpart exists. moreover, the concept of relevance of the description is emphasized and the relationships between intensional contexts and the dicotomy existing between value-free and value-loaded readings of definite descriptions is discussed. finally, the impact of the intermediate representation on the discourse history is considered, in order to explain how such a representation accounts for the contextual data and updates them according to the informational contents of the sentence being analyzed.
anticipating the reader's problems and the automatic generation of paraphrases. the notion of paraphrase is discussed and compared with the similar notion of periphrase. the role of paraphrases in oral communication is described, and the results of a study on the role of paraphrases in texts are given. finally, a system which models the use of paraphrases in texts is described.
a theory of lexical access in speech production. the generation of words in speech involves a number of processing stages. there is, first, a stage of conceptual preparation; this is followed by stages of lexical selection, phonological encoding, phonetic encoding and articulation. in addition, the speaker monitors the output and, if necessary, self-corrects. major parts of the theory have been computer modelled. the paper concentrates on experimental reaction time evidence in support of the theory.
conversion of a french surface expression into its semantic representation according to the reseda metalanguage. the work we describe here is a preliminary study concerning the automatic translation of natural language statements into the reseda semantic metalanguage. a first stage of the procedure consists in marking the "triggers", defined as lexical units which call upon one or more of the predicative patterns allowed for in the metalanguage. the predicative patterns obtained are then merged, and their case slots filled with the elements found in surface structure according to the predictions associated with the slots.
a quantifier scoping algorithm without a free variable constraint. three recent demands on quantifier scoping algorithms have been that they should be explicitly stated, they should be sound and complete with respect to the input sentence [hobbs and shieber 1987] and they should not employ a 'free variable constraint' [pereira 1989]. the first demand is for good academic practice. the second is to ask for an algorithm that generates all and only the possible scopings of a sentence. the third demand is for an algorithm that avoids appealing to the syntax of logical form in order to determine possible scopings. i present a modified version of [hobbs and shieber 1987], which simplifies its operation, and can be considered sound and complete, depending on what interpretations of english sentences are deemed possible. finally, any doubts concerning the use of logical form syntax are avoided.
learning dependencies between case frame slots. we address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. in particular, we propose a method of learning dependencies between case frame slots. we view the problem of learning case frame patterns as that of learning a multi-dimensional discrete joint distribution, where random variables represent case slots. we then formalize the dependencies between case slots as the probabilistic dependencies between these random variables. since the number of parameters in a multi-dimensional joint distribution is exponential in general, it is infeasible to accurately estimate them in practice. to overcome this difficulty, we settle with approximating the target joint distribution by the product of low order component distributions, based on corpus data. in particular we propose to employ an efficient learning algorithm based on the mdl principle to realize this task. our experimental results indicate that for certain classes of verbs, the accuracy achieved in a disambiguation experiment is improved by using the acquired knowledge of dependencies.
non-directionality and self-assessment in an example-based system using genetic algorithms. we show the application of an optimisation technique to natural language processing: genetic algorithms, thanks to the definition of a data structure called board and a formal distance. the system has two interesting features: non-directionality, which is more than bidirectionality, and self-assessment, independently of the inner knowledge. results of experiments are presented and discussed.
parsing long english sentences with pattern rules. in machine translation, parsing of long english sentences still causes some problems, whereas for short sentences a good machine translation system usually can generate readable translations. in this paper a practical method is presented for parsing long english sentences of some patterns. the rules for the patterns are treated separately from the augmented context free grammar, where each context free grammar rule is augmented by some syntactic functions and semantic functions. the rules for patterns and augmented context free grammar are complimentary to each other. in this way long english sentences covered by the patterns can be parsed efficiently.
saussurian analogy: a theoretical account and its application. in the cours de linguistique g&eacute;n&eacute;rale, saussure mentions a phenomenon of tremendous importance in language, analogy. for example, given the series walk, walked and look, how can we coin the fourth term, looked? we give a possible account of this phenomenon in terms of edition distances, thus paving the way to computational applications. this explanation accounts for prefixing, suffixing and infixing. we show how it is possible to perform the analogical analysis and generation of sentences, using a tree-bank and approximate pattern-matching. as a consequence, our proposal finds its place in the example-based approach to natural language processing.
learning question classifiers. in order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. these constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.this paper presents a machine learning approach to question classification. we learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. we show accurate results on a large collection of free-form questions used in trec 10.
the effectiveness of dictionary and web-based answer reranking. we describe an in-depth study of using a dictionary (wordnet) and web search engines (altavista, msn, and google) to boost the performance of an automated question answering system, webclopedia, in answering definition questions. the results indicate applying dictionary and web-based answer reranking together increase the performance of webclopedia on a set of 102 trec-10 definition questions by 25% in mean reciprocal rank score and 14% in finding answers in the top 5.