waashk/dblp huggingface dataset:
from submit to submitted via submission: on lexical rules in large-scale lexicon acquisition. this paper deals with the discovery, representation, and use of lexical rules (lrs) during large-scale semi-automatic computational lexicon acquisition. the analysis is based on a set of lrs implemented and tested on the basis of spanish and english business- and finance-related corpora. we show that, though the use of lrs is justified, they do not come cost-free. semi-automatic output checking is required, even with blocking and preemtion procedures built in. nevertheless, large-scope lrs are justified because they facilitate the unavoidable process of large-scale semi-automatic lexical acquisition. we also argue that the place of lrs in the computational process is a complex issue.
towards resolution of bridging descriptions. we present preliminary results concerning robust techniques for resolving bridging definite descriptions. we report our analysis of a collection of 20 wall street journal articles from the penn treebank corpus and our experiments with wordnet to identify relations between bridging descriptions and their antecedents.
polynomial time parsing of combinatory categorial grammars. in this paper we present a polynomial time parsing algorithm for combinatory categorial grammar. the recognition phase extends the cky algorithm for cfg. the process of generating a representation of the parse trees has two phases. initially, a shared forest is build that encodes the set of all derivation trees for the input string. this shared forest is then pruned to remove all spurious ambiguity.
information states as first class citizens. the information state of an agent is changed when a text (in natural language) is processed. the meaning of a text can be taken to be this information state change potential. the inference of a consequence make explicit something already implicit in the premises --- i.e. that no information state change occurs if the (assumed) consequence text is processed after the (given) premise texts have been processed. elementary logic (i.e. first-order logic) can be used as a logical representation language for texts, but the notion of a information state (a set of possibilities --- namely first-order models) is not available from the object language (belongs to the meta language). this means that texts with other texts as parts (e.g. propositional attitudes with embedded sentences) cannot be treated directly. traditional intensional logics (i.e. modal logic) allow (via modal operators) access to the information states from the object language, but the access is limited and interference with (extensional) notions like (standard) identity, variables etc. is introduced. this does not mean that the ideas present in intensional logics will not work (possibly improved by adding a notion of partiality), but rather that often a formalisation in the simple type theory (with sorts for entities and indices making information states first class citizens --- like individuals) is more comprehensible, flexible and logically well-behaved.
translation with cascaded finite state transducers. in this paper we discuss the use of cascaded finite state transducers for machine translation. a number of small, dedicated transducers is applied to convert sentence pairs from a bilingual corpus into generalized translation patterns. these patterns, together with the transducers are then used as a hierarchical translation memory for fully automatic translation. results on the german--english verbmobil corpus are given.
probing the lexicon in evaluating commercial mt systems. in the past the evaluation of machine translation systems has focused on single system evaluations because there were only few systems available. but now there are several commercial systems for the same language pair. this requires new methods of comparative evaluation. in the paper we propose a black-box method for comparing the lexical coverage of mt systems. the method is based on lists of words from different frequency classes. it is shown how these word lists can be compiled and used for testing. we also present the results of using our method on 6 mt systems that translate between english and german.
inducing german semantic verb classes from purely syntactic subcategorisation information. the paper describes the application of k-means, a standard clustering technique, to the task of inducing semantic classes for german verbs. using probability distributions over verb subcategorisation frames, we obtained an intuitively plausible clustering of 57 verbs into 14 classes. the automatic clustering was evaluated against independently motivated, hand-constructed semantic verb classes. a series of post-hoc cluster analyses explored the influence of specific frames and frame groups on the coherence of the verb classes, and supported the tight connection between the syntactic behaviour of the verbs and their lexical meaning components.
evaluating discourse processing algorithms. in order to take steps towards establishing a methodology for evaluating natural language systems, we conducted a case study. we attempt to evaluate two different approaches to anaphoric processing in discourse by comparing the accuracy and coverage of two published algorithms for finding the co-specifiers of pronouns in naturally occurring texts and dialogues. we present the quantitative results of hand-simulating these algorithms, but this analysis naturally gives rise to both a qualititive evaluation and recommendations for performing such evaluations in general. we illustrate the general difficulties encountered with quantitative evaluation. these are problems with: (a) allowing for underlying assumptions, (b) determining how to handle underspecifications, and (c) evaluating the contribution of false positives and error chaining.
learning optimal dialogue strategies: a case study of a spoken dialogue agent for email. this paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. while it is widely agreed that dialogue strategies should be formulated in terms of communicative intentions, there has been little work on automatically optimizing an agent's choices when there are multiple ways to realize a communicative intention. our method is based on a combination of learning algorithms and empirical evaluation techniques. the learning component of our method is based on algorithms for reinforcement learning, such as dynamic programming and q-learning. the empirical component uses the paradise evaluation framework (walker et al., 1997) to identify the important performance factors and to provide the performance function needed by the learning algorithm. we illustrate our method with a dialogue agent named elvis (email voice interactive system), that supports access to email over the phone. we show how elvis can learn to choose among alternate strategies for agent initiative, for reading messages, and for summarizing email folders.
paradise: a framework for evaluating spoken dialogue agents. this paper presents paradise (paradigm for dialogue system evaluation), a general framework for evaluating spoken dialogue agents. the framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.
quantitative and qualitative evaluation of darpa communicator spoken dialogue systems. this paper describes the application of the paradise evaluation framework to the corpus of 662 human-computer dialogues collected in the june 2000 darpa communicator data collection. we describe results based on the standard logfile metrics as well as results based on additional qualitative metrics derived using the date dialogue act tagging scheme. we show that performance models derived via using the standard metrics can account for 37% of the variance in user satisfaction, and that the addition of date metrics improved the models by an absolute 5%.
mixed initiative in dialogue: an investigation into discourse segmentation. conversation between two people is usually of mixed-initiative, with control over the conversation being transferred from one person to another. we apply a set of rules for the transfer of control to 4 sets of dialogues consisting of a total of 1862 turns. the application of the control rules lets us derive domain-independent discourse structures. the derived structures indicate that initiative plays a role in the structuring of discourse. in order to explore the relationship of control and initiative to discourse processes like centering, we analyze the distribution of four different classes of anaphora for two data sets. this distribution indicates that some control segments are hierarchically related to others. the analysis suggests that discourse participants often mutually agree to a change of topic. we also compared initiative in task oriented and advice giving dialogues and found that both allocation of control and the manner in which control is transferred is radically different for the two dialogue types. these differences can be explained in terms of collaborative planning principles.
understanding scene descriptions as event simulations. the language of scene descriptions must allow a hearer to build structures of schemas similar (to some level of detail) to those the speaker has built via perceptual processes. the understanding process in general requires a hearer to create and run <u>"event simulations"</u> to check the consistency and plausibility of a "picture" constructed from a speaker's description. a speaker must also run similar event simulations on his own descriptions in order to be able to judge when the hearer has been given sufficient information to construct an appropriate "picture", and to be able to respond appropriately to the hearer's questions about or responses to the scene description.in this paper i explore some simple scene description examples in which a hearer must make judgements involving reasoning about scenes, space, common-sense physics, cause-effect relationships, etc. while i propose some mechanisms for dealing with such scene descriptions, my primary concern at this time is to flesh out our understanding of just what the mechanisms must accomplish: what information will be available to them and what information must be found or generated to account for the inferences we know are actually made.
automatic english-chinese name transliteration for development of multilingual resources. in this paper, we describe issues in the translation of proper names from english to chinese which we have faced in constructing a system for multilingual text generation supporting both languages. we introduce an algorithm for mapping from english names to chinese characters based on (1) heuristics about relationships between english spelling and pronunciation, and (2) consistent relationships between english phonemes and chinese characters.
dual-coding theory and connectionist lexical selection. we introduce the bilingual dual-coding theory as a model for bilingual mental representation. based on this model, lexical selection neural networks are implemented for a connectionist transfer project in machine translation.
combining statistical and knowledge-based spoken language understanding in conditional models. spoken language understanding (slu) addresses the problem of extracting semantic meaning conveyed in an utterance. the traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. on the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. a generative hmm/cfg composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. the major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. we also study and compare conditional random fields (crfs) with perceptron learning for slu. experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the hmm/cfg model, which had already achieved an slu accuracy at the same level as the best results reported on the atis data.
an automatic treebank conversion algorithm for corpus sharing. an automatic treebank conversion method is proposed in this paper to convert a treebank into another treebank. a new treebank associated with a different grammar can be generated automatically from the old one such that the information in the original treebank can be transformed to the new one and be shared among different research communities. the simple algorithm achieves conversion accuracy of 96.4% when tested on 8,867 sentences between two major grammar revisions of a large mt system.
extracting key semantic terms from chinese speech query for web searches. this paper discusses the challenges and proposes a solution to performing information retrieval on the web using chinese natural language speech query. the main contribution of this research is in devising a divide-and-conquer strategy to alleviate the speech recognition errors. it uses the query model to facilitate the extraction of main core semantic string (css) from the chinese natural language speech query. it then breaks the css into basic components corresponding to phrases, and uses a multi-tier strategy to map the basic components to known phrases in order to further eliminate the errors. the resulting system has been found to be effective.
predicting intonational phrasing from text. determining the relationship between the intonational characteristics of an utterance and other features inferable from its text is important both for speech recognition and for speech synthesis. this work investigates the use of text analysis in predicting the location of intonational phrase boundaries in natural speech, through analyzing 298 utterances from the darpa air travel information service database. for statistical modeling, we employ classification and regression tree (cart) techniques. we achieve success rates of just over 90%, representing a major improvement over other attempts at boundary prediction from unrestricted text.
a fast, accurate deterministic parser for chinese. we present a novel classifier-based deterministic parser for chinese constituency parsing. our parser computes parse trees from bottom up in one pass, and uses classifiers to make shift-reduce decisions. trained and evaluated on the standard training and test sets, our best model (using stacked classifiers) runs in linear time and has labeled precision and recall above 88% using gold-standard part-of-speech tags, surpassing the best published results. our svm parser is 2-13 times faster than state-of-the-art parsers, while producing more accurate results. our maxent and dtree parsers run at speeds 40-270 times faster than state-of-the-art parsers, but with 5-6% losses in accuracy.
decoding algorithm in statistical machine translation. decoding algorithm is a crucial part in statistical machine translation. we describe a stack decoding algorithm in this paper. we present the hypothesis scoring method and the heuristics used in our algorithm. we report several techniques deployed to improve the performance of the decoder. we also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. we evaluate and compare these techniques/models in our statistical machine translation system.
modeling with structures in statistical machine translation. most statistical machine translation systems employ a word-based alignment model. in this paper we demonstrate that word-based alignment is a major cause of translation errors. we propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus. this new model achieved over 10% error reduction for our spoken language translation task.
word alignment for languages with scarce resources using bilingual corpora of other language pairs. this paper proposes an approach to improve word alignment for languages with scarce resources using bilingual corpora of other language pairs. to perform word alignment between languages l1 and l2, we introduce a third language l3. although only small amounts of bilingual data are available for the desired language pair l1-l2, large-scale bilingual corpora in l1-l3 and l2-l3 are available. based on these two additional corpora and with l3 as the pivot language, we build a word alignment model for l1 and l2. this approach can build a word alignment model for two languages even if no bilingual corpus is available in this language pair. in addition, we build another word alignment model for l1 and l2 using the small l1-l2 bilingual corpus. then we interpolate the above two models to further improve word alignment between l1 and l2. experimental results indicate a relative error rate reduction of 21.30% as compared with the method only using the small bilingual corpus in l1 and l2.
sinhala grapheme-to-phoneme conversion and rules for schwa epenthesis. this paper describes an architecture to convert sinhala unicode text into phonemic specification of pronunciation. the study was mainly focused on disambiguating /schwa/ and /a/ vowel epenthesis for consonants, which is one of the significant problems found in sinhala. this problem has been addressed by formulating a set of rules. the proposed set of rules was tested using 30,000 distinct words obtained from a corpus and compared with the same words manually transcribed to phonemes by an expert. the grapheme-to-phoneme (g2p) conversion model achieves 98% accuracy.
using leading text for news summaries: evaluation results and implications for commercial summarization applications. leading text extracts created to support some online boolean retrieval goals are evaluated for their acceptability as news document summaries. results are presented and discussed from the perspective of commercial summarization technology needs.
aligning articles in tv newscasts and newspapers. it is important to use pattern information (e.g. tv newscasts) and textual information (e.g. newspapers) together. for this purpose, we describe a method for aligning articles in tv newscasts and newspapers. in order to align articles, the alignment system uses words extracted from telops in tv newscasts. the recall and the precision of the alignment process are 97% and 89%, respectively. in addition, using the results of the alignment process, we develop a browsing and retrieval system for articles in tv newscasts and newspapers.
chunk-based statistical translation. this paper describes an alternative translation model based on a text chunk under the framework of statistical machine translation. the translation model suggested here first performs chunking. then, each word in a chunk is translated. finally, translated chunks are reordered. under this scenario of translation modeling, we have experimented on a broad-coverage japanese-english traveling corpus and achieved improved performance.
a pattern-based machine translation system extended by example-based processing. in this paper, we describe a machine translation system called palmtree which uses the "pattern based" approach as a fundamental framework. the pure pattern-based translation framework has several issues. one is the performance due to using many rules in the parsing stage, and the other is inefficiency of usage of translation patterns due to the exact-matching. to overcome these problems, we describe several methods; pruning techniques for the former, and introduction of example-based processing for the latter.
left-to-right target generation for hierarchical phrase-based translation. we present a hierarchical phrase-based statistical machine translation in which a target sentence is efficiently generated in left-to-right order. the model is a class of synchronous-cfg with a greibach normal form-like structure for the projected production rule: the paired target-side of a production rule takes a phrase prefixed form. the decoder for the target-normalized form is based on an early-style top down parser on the source side. the target-normalized form coupled with our top down parser implies a left-to-right generation of translations which enables us a straightforward integration with ngram language models. our model was experimented on a japanese-to-english newswire translation task, and showed statistically significant performance improvements against a phrase-based translation system.
discourse deixis: reference to discourse segments. computational approaches to discourse understanding have a two-part goal: (1) to identify those aspects of discourse understanding that require process-based accounts, and (2) to characterize the processes and data structures they involve. to date, in the area of reference, process-based accounts have been developed for subsequent reference via anaphoric pronouns and reference via definite descriptors. in this paper, i propose and argue for a process-based account of subsequent reference via deictic expressions. a significant feature of this account is that it attributes distinct mental reality to units of text often called discourse segments, a reality that is distinct from that of the entities described therein.
accommodating context change. two independent mechanisms of context change have been discussed separately in the literature - context change by entity introduction and context change by event simulation. here we discuss their integration. the effectiveness of the integration depends in part on a representation of events that captures people's uncertainty about their outcome - in particular, people's incomplete expectations about the changes effected by events. we propose such a representation and a process of accommodation that makes use of it, and discuss our initial implementation of these ideas.
discourse relations: a structural and presuppositional account using lexicalised tag. we show that discourse structure need not bear the full burden of conveying discourse relations by showing that many of them can be explained <i>nonstructurally</i> in terms of the grounding of <i>anaphoric presuppositions</i> (van der sandt, 1992). this simplifies discourse structure, while still allowing the realisation of a full range of discourse relations. this is achieved using the same semantic machinery used in deriving clause-level semantics.
prospects for computer-assisted dialect adaption. this paper describes a project which has explored the feasibility of using a computer to perform a significant portion of the changes required to adapt text from one dialect to several others. this ongoing experiment has examined adaptation between various dialects of quechua, finding that a computer program may be an important tool for adaptation. an experimental computer program was written and applied to text, and its output was field tested in five target dialects. preliminary results indicate that preprocessing text with a computer may 1) enable informants who are not bi-dialectical (in the source and target dialects) to produce adequate adaptations without much coaching from the linguist/translator; 2) improve the quality of the resulting text; and 3) reduce time and effortm-both in adaptation and in manuscript preparation.