waashk/dblp huggingface dataset:
deixis and conjunction in multimodal systems. in order to realize their full potential, multimodal interfaces need to support not just input from multiple modes, but single commands optimally distributed across the available input modes. a multimodal language processing architecture is needed to integrate semantic content from the different modes. johnston 1998a proposes a modular approach to multimodal language processing in which spoken language parsing is completed before multimodal parsing. in this paper, i will demonstrate the difficulties this approach faces as the spoken language parsing component is expanded to provide a compositional analysis of deictic expressions. i propose an alternative architecture in which spoken and multimodal parsing are tightly interleaved. this architecture greatly simplifies the spoken language parsing grammar and enables predictive information from spoken language parsing to drive the application of multimodal parsing and gesture combination rules. i also propose a treatment of deictic numeral expressions that supports the broad range of pen gesture combinations that can be used to refer to collections of objects in the interface.
finite-state multimodal parsing and understanding. multimodal interfaces require effective parsing and understanding of utterances whose content is distributed across multiple input modes. johnston 1998 presents an approach in which strategies for multimodal integration are stated declaratively using a unification-based grammar that is used by a multi-dimensional chart parser to compose inputs. this approach is highly expressive and supports a broad class of interfaces, but offers only limited potential for mutual compensation among the input modes, is subject to significant concerns in terms of computational complexity, and complicates selection among alternative multimodal interpretations of the input. in this paper, we present an alternative approach in which multimodal parsing and understanding are achieved using a weighted finite-state device which takes speech and gesture streams as inputs and outputs their joint interpretation. this approach is significantly more efficient, enables tight-coupling of multimodal understanding with speech recognition, and provides a general probabilistic framework for multimodal ambiguity resolution.
goal formulation based on communicative principles. the paper presents the constructive dialogue model as a new approach to formulate system goals in intelligent dialogue systems. the departure point is in general communicative principles which constrain cooperative and coherent communication. dialogue participants are engaged in a cooperative task whereby a model of the joint purpose is constructed. contributions are planned as reactions to the changing context, and no dialogue grammar is needed. also speech act classification is abandoned, in favour of contextual reasoning and rationality considerations.
exploring the role of punctuation in parsing natural text. few, if any, current nlp systems make any significant use of punctuation. intuitively, a treatment of punctuation seems necessary to the analysis and production of text. whilst this has been suggested in the fields of discourse structure, it is still unclear whether punctuation can help in the syntactic field. this investigation attempts to answer this question by parsing some corpus-based material with two similar grammars --- one including rules for punctuation, the other ignoring it. the punctuated grammar significantly out-performs the unpunctuated one, and so the conclusion is that punctuation can play a useful role in syntactic processing.
towards a syntactic account of punctuation. little notice has been taken of punctuation in the field of natural language processing, chiefly due to the lack of any coherent theory on which to base implementations. some work has been carried out concerning punctuation and parsing, but much of it seems to have been rather ad-hoc and performance-motivated. this paper describes the first step towards the construction of a theoretically-motivated account of punctuation. parsed corpora are processed to extract punctuation patterns, which are then checked and generalised to a small set of general punctuation rules. their usage is discussed, and suggestions are made for possible methods of including punctuation information in grammars.
toward a scoring function for quality-driven machine translation. we describe how we constructed an automatic scoring function for machine translation quality; this function makes use of arbitrarily many pieces of natural language processing software that has been designed to process english language text. by machine-learning values of functions available inside the software and by constructing functions that yield values based upon the software output, we are able to achieve preliminary, positive results in machine-learning the difference between human-produced english and machine-translation english. we suggest how the scoring function may be used for mt system development.
linguistically motivated descriptive term selection. a linguistically motivated approach to indexing, that is the provision of descriptive terms for texts of any kind, is presented and illustrated. the approach is designed to achieve good, i.e. accurate and flexible, indexing by identifying index term sources in the meaning representations built by a powerful general purpose analyser, and providing a range of text expressions constituting semantic and syntactic variants for each term concept. indexing is seen as a legitimate form of shallow text processing, but one requiring serious semantically based language processing, particularly to obtain well-founded complex terms, which is the main objective of the project described. the type of indexing strategy described is further seen as having utility in a range of applications environments.
processing of sentences with intra-sentential code-switching. speakers of certain bilingual communities systematically produce utterances in which they switch from one language to another, suggesting that the two language systems systematically interact with each other in the production (and recognition) of these sentences. we have investigated this phenomenon in a formal or computational framework which consists of two grammatical systems and a mechanism for switching between the two systems. a variety of constraints apparent in these sentences are then explained in terms of constraints on the switching mechanism, especially, those on closed class items.
an english to korean transliteration model of extended markov window. automatic transliteration problem is to transcribe foreign words in one's own alphabet. machine generated transliteration can be useful in various applications such as indexing in an information retrieval system and pronunciation synthesis in a text-to-speech system. in this paper we present a model for statistical english-to-korean transliteration that generates transliteration candidates with probability. the model is designed to utilize various information sources by extending a conventional markov window. also, an efficient and accurate method for alignment and syllabification of pronunciation units is described. the experimental results show a recall of 0.939 for trained words and 0.875 for untrained words when the best 10 candidates are considered.
markov random field based english part-of-speech tagging system. probabilistic models have been widely used for natural language processing. part-of-speech tagging, which assingns the most likely tag to each word in a given sentence, is one of the problems which can be solved by statistical approach. many researchers have tried to solve the problem by hidden markov model (hmm), which is well known as one of the statistical models. but it has many difficulties: integrating heterogeneous information, coping with data sparseness problem, and adapting to new environments. in this paper, we propose a markov radom field (mrf) model based approach to the tagging problem. the mrf provides the base frame to combine various statistical information with maximum entropy (me) method. as gibbs distribution can be used to describe a posteriori probability of tagging, we use it in maximum a posteriori (map) estimation of optimizing process. besides, several tagging models are developed to show the effect of adding information. experimental results show that the performance of the tagger gets improved as we add more statistical information, and that mrf-based tagging model is better than hmm based tagging in data sparseness problem.
a statistical approach to machine aided translation of terminology banks. this paper reports on a new statistical approach to machine aided translation of terminology bank. the text in the bank is hyphenated and then dissected into roots of 1 to 3 syllables. both hyphenation and dissection are done with a set of initial probabilities of syllables and roots. the probabilities are repeatedly revised using an em algorithm. after each iteration of hyphenation or dissection, the resulting syllables and roots are counted subsequently to yield more precise estimation of probability. the set of roots rapidly converges to a set of most likely roots. preliminary experiments have shown promising results. from a terminology bank of more than 4, 000 terms, the algorithm extracts 223 general and chemical roots, of which 91% are actually roots. the algorithm dissects a word into roots with around 86% hit rate. the set of roots and their hand-translation are then used in a compositional translation of the terminology bank. one can expect the translation of terminology bank using this approach to be more cost-effective, consistent, and with a better closure.
coordination in reconnaissance-attack parsing. a proposal for recognizing coordinate structures using the 'reconnaissance-attack' model is presented. the approach concentrates on distinguishing predicate coordination from other types of coordination and suggests that low-level structural cues (such as the number of predicates, coordinators, and subordinators occurring in the input string) can be exploited at little cost during the early phase of the parse, with dramatic results. the method is tested on a text of 16,000 words.
robust segmentation of japanese text into a lattice for parsing. we describe a segmentation component that utilizes minimal syntactic knowledge to produce a lattice of word candidates for a broad coverage japanese nl parser. the segmenter is a finite state morphological analyzer and text normalizer designed to handle the orthographic variations characteristic of written japanese, including alternate spellings, script variation, vowel extensions and word-internal parenthetical material. this architecture differs from conventional japanese wordbreakers in that it does not attempt to simultaneously attack the problems of identifying segmentation candidates and choosing the most probable analysis. to minimize duplication of effort between components and to give the segmenter greater freedom to address orthography issues, the task of choosing the best analysis is handled by the parser, which has access to a much richer set of linguistic information. by maximizing recall in the segmenter and allowing a precision of 34.7%, our parser currently achieves a breaking accuracy of ~97% over a wide variety of corpora.
automatic thesaurus generation through multiple filtering. in this paper, we propose a method of generating bilingual keyword clusters or thesauri from parallel or comparable bilingual corpora. the method combines morphological and lexical processing, bilingual word aligmnent, and graph-theoretic cluster generation. an experiment shows that the method is promising.
an efficient execution method for rule-based machine translation. a rule based system is an effective way to implement a machine translation system because of its extensibility and maintainability. however, it is disadvantageous in processing efficiency. in a rule based machine translation system, the grammar consists of a lot of rewriting rules. while the translation is carried out by repeating pattern matching and transformation of graph structures, most rules fail in pattern matching. it is to be desired that pattern matching of the unfruitful rules should be avoided. this paper proposes a method to restrict the rule application by activating rules dynamically. the logical relationship among rules are pre-analyzed and a set of antecedent actions, which are prerequisite for the condition of the rule being satisfied, is determined for each rule. in execution time, a rule is activated only when one of the antecedent actions are carried out. the probability of a rule being activated is reduced to near the occurrence probability of its relevant linguistic phenomenon. as most rules relate to linguistic phenomena that rarely occur, the processing efficiency is drastically improved.
extracting word correspondences from bilingual corpora based on word co-occurrence information. a new method has been developed for extracting word correspondences from a bilingual corpus. first, the co-occurrence information for each word in both languages is extracted from the corpus. then, the correlations between the co-occurrence features of the words are calculated pairwisely with the assistance of a basic word bilingual dictionary. finally, the pairs of words with the highest correlations are output selectively. this method is applicable to rather small, unaligned corpora; it can extract correspondences between compound words as well as simple words. an experiment using bilingual patent-specification corpora achieved 28% recall and 76% precision; this demonstrates that the method effectively reduces the cost of bilingual dictionary augmenntation.
learning translation templates from bilingual text. this paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. this method improves translation quality and facilitates customization of machine translation systems. this paper focuses on the automatic learning of translation templates. a translation template is a bilingual pair of sentences in which corresponding units (words and pharases) are coupled and replaced with variables. correspondence between units is determined by suing a bilingual dictionary and by analyzing the syntactic structure of the sentences. syntactic ambiguity and ambiguity in correspondence between units are simultaneously resolved. all of the translation templates generated from a bilingual corpus are grouped by their source language part, and then further refined to resolved conflicts among templates whose source language parts are the same but whose target language parts are different. by using the proposed method, not only transfer rules but also knowledge for lexical selection is effectively extracted from a bilingual corpus.
unsupervised word sense disambiguation using bilingual comparable corpora. an unsupervised method for word sense disambiguation using a bilingual comparable corpus was developed. first, it extracts statistically significant pairs of related words from the corpus of each language. then, aligning pairs of related words translingually, it calculates the correlation between the senses of a first-language polysemous word and the words related to the polysemous word, which can be regarded as clues for determining the most suitable sense. finally, for each instance of the polysemous word, it selects the sense that maximizes the score, i.e., the sum of the correlations between each sense and the clues appearing in the context of the instance. to overcome both the problem of ambiguity in the translingual alignment of pairs of related words and that of disparity of topical coverage between corpora of different languages, an algorithm for calculating the correlation between senses and clues iteratively was devised. an experiment using wall street journal and nihon keizai shimbun corpora showed that the new method has promising performance; namely, the applicability and precision of its sense selection are 88.5% and 77.7%, respectively, averaged over 60 test polysemous words.
corpus-dependent association thesauri for information retrieval. this paper presents a method for automatically generating an association thesaurus from a text corpus, and demonstrates its application to information retrieval. the thesaurus generation method consists of extracting terms and co-occurrence data from a corpus and analyzing the correlation between terms statistically. a new method for disambiguating the structure of compound nouns, which is a key component for term extraction, is also proposed. the automatically generated thesaurus is effectively used as a tool for exploring information. a thesaurus navigator having novel functions such as term clustering, thesaurus overview, and zooming-in is proposed.
completion of japanese sentences by inferring function words from content words. a method of generating a japanese sentence by inferring function words from content words using valency patterns is presented.a procedure for selecting an appropriate function word, on the assumption that correct content words have been selected for a given phrase lattice, is described. a method of inferring a correct verb when verbs are recognized less accurately than nouns by the speech recognition system is described. sentences are produced from content words as inputs by using the valency patterns obtained from collected dialogue sentences in a restricted task domain. using the semantic features of preceding nouns and valency patterns allow a fairly restricted number of candidate verbs to be inferred.this method eliminates possible errors at the interface between speech recognition and machine translation (component technologies of an automatic telephone interpretation system) and selects the most appropriate candidate from a lattice of typical phrases output by the speech recognition system.
a response to the need for summary responses. in this paper we argue that natural language interfaces to databases should be able to produce summary responses as well as listing actual data. we describe a system (incorporating a number of heuristics and a knowledge base built on top of the database) that has been developed to generate such summary responses. it is largely domain-independent, has been tested on many examples, and handles a wide variety of situations where summary responses would be useful.
"the first million is hardest to get": building a large tagged corpus as automatically as possible. the paper describes a recently started project in sweden. the goal of the project is to produce a corpus of (at least) one million words of running text from different genres, where all words are classified for word class and for a set of morpho-syntactic properties. a set of methods and tools for automating the process are being developed and will be presented, and problems and some solutions in connection with e.g. homography disambiguation will be discussed.
linguistic indeterminacy as a source of errors in tagging. most evaluations of part-of-speech tagging compare the utput of an automatic tagger to some established standard, define the differences as tagging errors and try to remedy them by, e.g., more training of the tagger. the present article is based on a manual analysis of a large number of tagging errors. some clear patterns among the errors can be discerned, and the sources of the errors as well as possible alternative methods of remedy are presented and discussed. in particular are the problems with undecidable cases treated.
a portable & quick japanese parser: qjp. qjp is a portable and quick software module for japanese processing. qjp analyzes a japanese sentence into segmented morphemes/words with tags and a syntactic bunsetsu kakari-uke structure based on the two strategies, a) morphological analysis based on character-types and functional-words and b) syntactic analysis by simple treatment of structural ambiguities and ignoring semantic information. qjp is small, fast and robust, because 1) dictionary size (less than 100kb) and required memory size (260kb) are very small, 2) analysis speed is fast (more than 100 words/sec on 80486-pc), and 3) even a 100-word long sentence containing unknown words is easily processed.using qjp and its analysis results as a base and adding other functions for processing japanese documents, a variety of applications can be developed on unix workstations or even on pcs.
a discrete model of degree concept in natural language. degree words in natural language, such as 'often' and 'sometimes,' do not have denotations in the real world. this causes some interesting characteristics for degree words. for example, the correspondence between the english word 'often' and the intnitively corresponding japanese word is not obvious. this paper proposes a conceptual representation to describe a wide range of linguistic phenomena which are related to degree concepts in natural language.
lexical information for determining japanese unbounded dependency. this paper presents a practical method for a global structure analyzing algorithm of japanese long sentences with lexical information, a method which we call lexical discourse grammar (ldg). this method assumes that japanese function words, such as conjunctive particles (postpositions) located at the end of each clause, have modality and suggest global structures of japanese long sentences in cooperation with modality within predicates or auxiliary verbs. ldg classifies the encapsulating powers of function words into six levels, and modality in predicates into four types. ldg presumes the inter-clausal dependency within japanese sentences prior to syntactic and semantic analyses, by utilizing the differences of the encapsulating powers each japanese function word has, and by utilizing modification preference between function words and predicates that reflects consistency of modality in them. in order to confirm the encapsulation power of japanese function words, we analyzed the speech utterances of a male announcer and found the correlation between a particle's encapsulating power and the pause length inserted after the clause with a conjunctive particle.
a hybrid japanese parser with hand-crafted grammar and statistics. this paper describes a hybrid parsing method for japanese which uses both a hand-crafted grammar and a statistical technique. the key feature of our system is that in order to estimate likelihood for a parse tree, the system uses information taken from alternative partial parse trees generated by the grammar. this utilization of alternative trees enables us to construct a new statistical model called triplet/quadruplet model. we show that this model can capture a certain tendency in japanese syntactic structures and this point contributes to improvement of parsing accuracy on a shallow level. we report that, with an underspecified hpsg-based grammar and a maximum entropy estimation, our parser achieved high accuracy: 88.6% accuracy in dependency analysis of the edr annotated corpus, and that it outperformed other purely statistical parsing methods on the same corpus. this result suggests that proper treatment of hand-crafted grammars can contribute to parsing accuracy on a shallow level.