waashk/dblp huggingface dataset:
towards an optimal lexicalization in a natural-sounding portable natural language generator for dialog systems. in contrast to the latest progress in speech recognition, the state-of-the-art in natural language generation for spoken language dialog systems is lagging behind. the core dialog managers are now more sophisticated; and natural-sounding and flexible output is expected, but not achieved with current simple techniques such as template-based systems. portability of systems across subject domains and languages is another increasingly important requirement in dialog systems. this paper presents an outline of legend, a system that is both portable and generates natural-sounding output. this goal is achieved through the novel use of existing lexical resources such as framenet and wordnet.
the berkeley framenet project. framenet is a three-year nsf-supported project in corpus-based computational lexicography, now in its second year (nsf iri-9618838, "tools for lexicon building"). the project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. the resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between "frame elements" and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). this report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.
on representing governed prepositions and handling "incorrect" and novel prepositions. nlp systems, in order to be robust, must handle novel and ill-formed input. one common type of error involves the use of non-standard prepositions to mark arguments. in this paper, we argue that such errors can be handled in a systematic fashion, and that a system designed to handle them offers other advantages. we offer a classification scheme for preposition usage errors. further, we show how the knowledge representation employed in the sra nlp system facilitates handling these data.
coupling ccg and hybrid logic dependency semantics. categorial grammar has traditionally used the &lambda;-calculus to represent meaning. we present an alternative, dependency-based perspective on linguistic meaning and situate it in the computational setting. this perspective is formalized in terms of hybrid logic and has a rich yet perspicuous propositional ontology that enables a wide variety of semantic phenomena to be represented in a single meaning formalism. finally, we show how we can couple this formalization to combinatory categorial grammar to produce interpretations compositionally.
low-cost, high-performance translation retrieval: dumber is better. in this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of a translation memory system. we take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character and word-segmented data, in combination with a range of local segment contiguity models (in the form of n-grams). over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word n-gram models. further, in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. we also provide evidence that our findings are scalable.
discriminative word alignment with conditional random fields. in this paper we present a novel approach for inducing word alignments from sentence aligned data. we use a conditional random field (crf), a discriminative model, which is estimated on a small supervised training set. the crf is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. moreover, the crf has efficient training and decoding processes which both find globally optimal solutions.we apply this alignment model to both french-english and romanian-english language pairs. we show how a large number of highly predictive features can be easily incorporated into the crf, and demonstrate that even with only a few hundred word-aligned training sentences, our model improves over the current state-of-the-art with alignment error rates of 5.29 and 25.8 for the two tasks respectively.
learning the countability of english nouns from corpus data. this paper describes a method for learning the countability preferences of english nouns from raw text corpora. the method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 countability classes. we were able to assign countability to english nouns with a precision of 94.6%.
multiple underlying systems: translating user requests into programs to produce answers. a user may typically need to combine the strengths of more than one system in order to perform a task. in this paper, we describe a component of the janus natural language interface that translates intensional logic expressions representing the meaning of a request into executable code for each application program, chooses which combination of application systems to use, and designs the transfer of data among them in order to provide an answer. the complete janus natural language system has been ported to two large command and control decision support aids.
an improved parser for data-oriented lexical-functional analysis. we present an lfg-dop parser which uses fragments from lfg-annotated sentences to parse new sentences. experiments with the verbmobil and homecentre corpora show that (1) viterbi n best search performs about 100 times faster than monte carlo search while both achieve the same accuracy; (2) the dop hypothesis which states that parse accuracy increases with increasing fragment size is confirmed for lfg-dop; (3) lfg-dop's relative frequency estimator performs worse than a discounted frequency estimator; and (4) lfg-dop significantly outperforms tree-dop if evaluated on tree structures only.
negative polarity licensing at the syntax-semantics interface. recent work on the syntax-semantics interface (see e.g. (dalrymple et al., 1994)) uses a fragment of linear logic as a 'glue language' for assembling meanings compositionally. this paper presents a glue language account of how negative polarity items (e.g. ever, any) get licensed within the scope of negative or downward-entailing contexts (ladusaw, 1979), e.g. nobody ever left. this treatment of licensing operates precisely at the syntax-semantics interface, since it is carried out entirely within the interface glue language (linear logic). in addition to the account of negative polarity licensing, we show in detail how linear-logic proof nets (girard, 1987; gallier, 1992) can be used for efficient meaning deduction within this 'glue language' framework.
a general computational treatment of comparatives for natural language question answering. we discuss the techniques we have developed and implemented for the cross-categorial treatment of comparatives in teli, a natural language question-answering system that's transportable among both application domains and types of backend retrieval systems. for purposes of illustration, we shall consider the example sentences "list the cars at least 20 inches more than twice as long as the century is wide" and "have any us companies made at least 3 more large cars than buick?" issues to be considered include comparative inflections, left recursion and other forms of nesting, extraposition of comparative complements, ellipsis, the wh element "how", and the translation of normalized parse trees into logical form.
what is the minimal set of fragments that achieves maximal parse accuracy? we aim at finding the minimal set of fragments which achieves maximal parse accuracy in data oriented parsing. experiments with the penn wall street journal treebank show that counts of almost arbitrary fragments within parse trees are important, leading to improved parse accuracy over previous models tested on this treebank (a precision of 90.8% and a recall of 90.6%). we isolate some dependency relations which previous models neglect but which contribute to higher parse accuracy.
integrating word boundary identification with sentence understanding. chinese sentences are written with no special delimiters such as space to indicate word boundaries. existing chinese nlp systems therefore employ preprocessors to segment sentences into words. contrary to the conventional wisdom of separating this issue from the task of sentence understanding, we propose an integrated model that performs word boundary identification in lockstep with sentence understanding. in this approach, there is no distinction between rules for word boundary identification and rules for sentence understanding. these two functions are combined. word boundary ambiguities are detected, especially the fallacious ones, when they block the primary task of discovering the inter-relationships among the various constituents of a sentence, which essentially is the essence of the understanding process. in this approach, statistical information is also incorporated, providing the system a quick and fairly reliable starting ground to carry out the primary task of relationship-building.
learning the structure of task-driven human-human dialogs. data-driven techniques have been used for many computational linguistics tasks. models derived from data are generally more robust than hand-crafted systems since they better reflect the distribution of the phenomena being modeled. with the availability of large corpora of spoken dialog, dialog management is now reaping the benefits of data-driven techniques. in this paper, we compare two approaches to modeling subtask structure in dialog: a chunk-based model of subdialog sequences, and a parse-based, or hierarchical, model. we evaluate these models using customer agent dialogs from a catalog service domain.
responding to user queries in a collaborative environment. we propose a plan-based approach for responding to user queries in a collaborative environment. we argue that in such an environment, the system should not accept the user's query automatically, but should consider it a proposal open for negotiation. in this paper we concentrate on cases in which the system and user disagree, and discuss how this disagreement can be detected, negotiated, and how final modifications should be made to the existing plan.
a bottom-up approach to sentence ordering for multi-document summarization. ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. in multi-document summarization, information is selected from a set of source documents. however, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. therefore, it is vital to properly order the information in multi-document summarization. we present a bottom-up approach to arrange sentences extracted for multi-document summarization. to capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. these criteria are integrated into a criterion by a supervised learning approach. we repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. we evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. we introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.
anchoring floating quantifiers in japanese-to-english machine transltion. in this paper we present an algorithm to anchor floating quantifiers in japanese, a language in which quantificational nouns and numeral-classifier combinations can appear separated from the noun phrase they quantify. the algorithm differentiates degree and event modifiers from nouns that quantify noun phrases. it then finds a suitable anchor for such floating quantifiers. to do this, the algorithm considers the part of speech of the quantifier and the target, the semantic relation between them, the case marker of the antecedent and the meaning of the verb that governs the two constituents. the algorithm has been implemented and tested in a rule-based japanese-to-english machine translation system, with an accuracy of 76% and a recall of 97%.
a dop model for semantic interpretation. in data-oriented language processing, an annotated language corpus is used as a stochastic grammar. the most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. this approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the penn tree-bank. if a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. the present paper explains this semantic interpretation method. a data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the english atis corpus and the dutch ovis corpus. experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.
managing information at linguistic interfaces. a large spoken dialogue translation system imposes both engineering and linguistic constraints on the way in which linguistic information is communicated between modules. we describe the design and use of interface terms, whose formal, functional and communicative role has been tested in a sequence of integrated systems and which have proven adequate to these constraints.
shallow parsing on the basis of words only: a case study. we describe a case study in which a memory-based learning algorithm is trained to simultaneously chunk sentences and assign grammatical function tags to these chunks. we compare the algorithm's performance on this parsing task with varying training set sizes (yielding learning curves) and different input representations. in particular we compare input consisting of words only, a variant that includes word form information for low-frequency words, gold-standard pos only, and combinations of these. the word-based shallow parser displays an apparently log-linear increase in performance, and surpasses the flatter pos-based curve at about 50,000 sentences of training data. the low-frequency variant performs even better, and the combinations is best. comparative experiments with a real pos tagger produce lower results. we argue that we might not need an explicit intermediate pos-tagging step for parsing when a sufficient amount of training material is available and word form information is used for low-frequency words.
memory-based morphological analysis. we present a general architecture for efficient and deterministic morphological analysis based on memory-based learning, and apply it to morphological analysis of dutch. the system makes direct mappings from letters in context to rich categories that encode morphological boundaries, syntactic class labels, and spelling changes. both precision and recall of labeled morphemes are over 84% on held-out dictionary test words and estimated to be over 93% in free text.
detecting problematic turns in human-machine interactions: rule-induction versus memory-based learning approaches. we address the issue of on-line detection of communication problems in spoken dialogue systems. the usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. by applying both rule-induction and memory-based learning techniques to data obtained with a dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline. the results are interesting from a dialogue perspective since they employ features that are present in the majority of spoken dialogue systems and can be obtained with little or no computational overhead. the results are interesting from a machine learning perspective, since they show that the rule-based method performs significantly better than the memory-based method, because the former is better capable of representing interactions between features.
simulating children's null subjects: an early language generation model. this paper reports work in progress on a sentence generation model which attempts to emulate certain language output patterns of children between the ages of one and one-half and three years. in particular, the model addresses the issue of why missing or phonetically "null" subjects appear as often as they do in the speech of young english-speaking children. it will also be used to examine why other patterns of output appear in the speech of children learning languages such as italian and chinese. initial findings are that an output generator successfully approximates the null-subject output patterns found in english-speaking children by using a 'processing overload' metric alone; however, reference to several parameters related to discourse orientation and agreement morphology is necessary in order to account for the differing patterns of null arguments appearing cross-linguistically. based on these findings, it is argued that the 'null-subject phenomenon' is due to the combined effects of limited processing capacity and early, accurate parameter setting.
a quantitative analysis of lexical differences between genders in telephone conversations. in this work, we provide an empirical analysis of differences in word use between genders in telephone conversations, which complements the considerable body of work in sociolinguistics concerned with gender linguistic differences. experiments are performed on a large speech corpus of roughly 12000 conversations. we employ machine learning techniques to automatically categorize the gender of each speaker given only the transcript of his/her speech, achieving 92% accuracy. an analysis of the most characteristic words for each gender is also presented. experiments reveal that the gender of one conversation side influences lexical use of the other side. a surprising result is that we were able to classify male-only vs. female-only conversations with almost perfect accuracy.
another facet of lig parsing. in this paper we present a new parsing algorithm for linear indexed grammars (ligs) in the same spirit as the one described in (vijay-shanker and weir, 1993) for tree adjoining grammars. for a lig l and an input string x of length n, we build a non ambiguous context-free grammar whose sentences are all (and exclusively) valid derivation sequences in l which lead to x. we show that this grammar can be built in o(n6) time and that individual parses can be extracted in linear time with the size of the extracted parse tree. though this o(n6) upper bound does not improve over previous results, the average case behaves much better. moreover, practical parsing times can be decreased by some statically performed computations.
defaults in unification grammar. incorporation of defaults in grammar formalisms is important for reasons of linguistic adequacy and grammar organization. in this paper we present an algorithm for handling default information in unification grammar. the algorithm specifies a logical operation on feature structures, merging with the non-default structure only those parts of the default feature structure which are not constrained by the non-default structure. we present various linguistic applications of default unification.
constraint-based categorical grammar. we propose a generalization of categorial grammar in which lexical categories are defined by means of recursive constraints. in particular, the introduction of relational constraints allows one to capture the effects of (recursive) lexical rules in a computationally attractive manner. we illustrate the linguistic merits of the new approach by showing how it accounts for the syntax of dutch cross-serial dependencies and the position and scope of adjuncts in such constructions. delayed evaluation is used to process grammars containing recursive constraints.
a morphographemic model for error correction in nonconcatenative strings. this paper introduces a spelling correction system which integrates seamlessly with morphological analysis using a multi-tape formalism. handling of various semitic error problems is illustrated, with reference to arabic and syriac examples. the model handles errors vocalisation, diacritics, phonetic syncopation and morphographemic idiosyncrasies, in addition to damerau errors. a complementary correction strategy for morphologically sound but morphosyntactically ill-formed words is outlined.
deriving the predicate-argument structure for a free word order language. in relatively free word order languages, grammatical functions are intricately related to case marking. assuming an ordered representation of the predicate-argument structure, this work proposes a combinatory categorial grammar formulation of relating surface case cues to categories and types for correctly placing the arguments in the predicate-argument structure. this is achieved by treating case markers as type shifters. unlike other cg formulations, type shifting does not proliferate or cause spurious ambiguity. categories of all argument-encoding grammatical functions follow from the same principle of category assignment. normal order evaluation of the combinatory form reveals the predicate-argument structure. the application of the method to turkish is shown.
the logical structure of binding. a logical recasting of binding theory is performed as an enhancing step for the purpose of its full and lean declarative implementation. a new insight on sentential anaphoric processes is presented which may suggestively be captured by the slogan binding conditions are the effect of phase quantification on the universe of discourse referents.
tagset reduction without information loss. a technique for reducing a tagset used for n-gram part-of-speech disambiguation is introduced and evaluated in an experiment. the technique ensures that all information that is provided by the original tagset can be restored from the reduced one. this is crucial, since we are interested in the linguistically motivated tags for part-of-speech disambiguation. the reduced tagset needs fewer parameters for its statistical model and allows more accurate parameter estimation. additionally, there is a slight but not significant improvement of tagging accuracy.
a simplified theory of tense representations and constraints on their composition. this paper proposes a set of representations for tenses and a set of constraints on how they can be combined in adjunct clauses. the semantics we propose explains the possible meanings of tenses in a variety of sentential contexts. it also supports an elegant constraint on tense combination in adjunct clauses. these semantic representations provide insights into the interpretations of tenses, and the constraints provide a source of syntactic disambiguation that has not previously been demonstrated. we demonstrate an implemented disambiguator for a certain class of three-clause sentences based on our theory.