waashk/dblp huggingface dataset:
bilingual hebrew-english generation of possessives and partitives: raising the input abstraction level. we present a contrastive analysis of the syntactic realizations of possessives and partitives in hebrew and english and conclude by presenting an input specification for complex nps which is slightly more abstract than the one used in surge. we define two main features - <i>possessor</i> and <i>ref-set</i> and discuss how the grammar handles complex syntactic co-occurrence phenomena based on this input. we conclude by evaluating how the resulting input specification language is appropriate for both languages. syntactic realization grammars have traditionally attempted to accept inputs with the highest possible level of abstraction, in order to facilitate the work of the components (sentence planner) preparing the input. recently, the search for higher abstraction has been, however, challenged (elhadad and robin, 1996) (lavoie and rambow, 1997) (busemann and horacek, 1998). in this paper, we contribute to the issue of selecting the "ideal" abstraction level in the input to syntactic realization grammar by considering the case of partitives and possessives in a bilingual hebrew-english generation grammar. in the case of bilingual generation, the ultimate goal is to provide a single input structure, where only the openclass lexical entries are specific to the language. in that case, the minimal abstraction required must cover the different syntactic constraints of the two languages.
automatic alignment in parallel corpora. this paper addresses the alignment issue in the framework of exploitation of large bimultilingual corpora for translation purposes. a generic alignment scheme is proposed that can meet varying requirements of different applications. depending on the level at which alignment is sought, appropriate surface linguistic information is invoked coupled with information about possible unit delimiters. each text unit (sentence, clause or phrase) is represented by the sum of its content tags. the results are then fed into a dynamic programming framework that computes the optimum alignment of units. the proposed scheme has been tested at sentence level on parallel corpora of the celex database. the success rate exceeded 99%. the next steps of the work concern the testing of the scheme's efficiency at lower levels endowed with necessary bilingual information about potential delimiters.
the complexity of recognition of linguistically adequate dependency grammars. results of computational complexity exist for a wide range of phrase structure-based grammar formalisms, while there is an apparent lack of such results for dependency-based formalisms. we here adapt a result on the complexity of id/lp-grammars to the dependency framework. contrary to previous studies on heavily restricted dependency grammars, we prove that recognition (and thus, parsing) of linguistically adequate dependency grammars is np-complete.
learning event durations from event descriptions. we have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. here we describe a method for measuring inter-annotator agreement for these event duration distributions. we then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.
learning noun phrase anaphoricity to improve conference resolution: issues in representation and optimization. knowledge of the anaphoricity of a noun phrase might be profitably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. perhaps surprisingly, recent attempts to incorporate automatically acquired anaphoricity information into coreference systems, however, have led to the degradation in resolution performance. this paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. in particular, we present a new corpus-based approach to anaphoricity determination. experiments on three standard coreference data sets demonstrate the effectiveness of our approach.
instance-based sentence boundary determination by optimization for natural language generation. this paper describes a novel instance-based sentence boundary determination method for natural language generation that optimizes a set of criteria based on examples in a corpus. compared to existing sentence boundary determination approaches, our work offers three significant contributions. first, our approach provides a general domain independent framework that effectively addresses sentence boundary determination by balancing a comprehensive set of sentence complexity and quality related constraints. second, our approach can simulate the characteristics and the style of naturally occurring sentences in an application domain since our solutions are optimized based on their similarities to examples in a corpus. third, our approach can adapt easily to suit a natural language generation system's capability by balancing the strengths and weaknesses of its subcomponents (e.g. its aggregation and referring expression generation capability). our final evaluation shows that the proposed method results in significantly better sentence generation outcomes than a widely adopted approach.
a sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". to determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. we address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). this task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".we first evaluate human performance at the task. then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. we show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of svms when we employ a novel similarity measure appropriate to the problem.
inducing ontological co-occurrence vectors. in this paper, we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as wordnet. we evaluate the framework on the task of automatically attaching new concepts into the ontology. experimental results show 73.9% attachment accuracy in the first position and 81.3% accuracy in the top-5 positions. this framework could potentially serve as a foundation for ontologizing lexical-semantic resources and assist the development of other largescale and internally consistent collections of semantic information.
an unsupervised approach to prepositional phrase attachment using contextually similar words. prepositional phrase attachment is a common source of ambiguity in natural language processing. we present an unsupervised corpus-based approach to prepositional phrase attachment that achieves similar performance to supervised methods. unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative process to extract training data from an automatically parsed corpus. attachment decisions are made using a linear combination of features and low frequency events are approximated using contextually similar words.
espresso: leveraging generic patterns for automatically harvesting semantic relations. in this paper, we present espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. the main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. we present an empirical comparison of espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision.
bleu: a method for automatic evaluation of machine translation. human evaluations of machine translation are extensive but expensive. human evaluations can take months to finish and involve human labor that can not be reused. we propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. we present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
possissive pronominal anaphor resolution in portuguese written texts. this paper describes a proposal for portuguese possessive pronominal anaphor (ppa) resolution, a problem little considered so far. particularly, we address the problem of portuguese 3rd person intrasentential ppas seu/sua/seus/suas (his/her/their/its, for human and non-human subjects in english), which constitute 30% of pronominal occurrences in our corpus (brazilian laws about environment protection). considering some differences between ppas and other kinds of anaphors, such as personal or demonstrative pronouns, we define three knowledge sources (kss) for ppa resolution: surface patterns (taking in account factors such as syntactic parallelism), possessive relationship rules and sentence centering. these knowledge sources are organized in a blackboard architecture for ppa resolution, which provides both knowledge and procedure distribution among autonomous entities (reflexive agents), each of them specialized in a particular aspect of the problem solving. the proposal has been implemented and its results are discussed at the end of the work.
multiset-valued linear index grammars: imposing dominance constraints on derivations. this paper defines multiset-valued linear index grammar and unordered vector grammar with dominance links. the former models certain uses of multiset-valued feature structures in unification-based formalisms, while the latter is motivated by word order variation and by "quasi-trees", a generalization of trees. the two formalisms are weakly equivalent, and an important subset is at most context-sensitive and polynomially parsable.
a definite clause version of categorial grammar. we introduce a first-order version of categorial grammar, based on the idea of encoding syntactic types as definite clauses. thus, we drop all explicit requirements of adjacency between combinable constituents, and we capture word-order constraints simply by allowing subformulae of complex types to share variables ranging over string positions. we are in this way able to account for constructions involving discontinuous constituents. such constructions involving discontinuous constituents. such constructions are difficult to handle in the more traditional version of categorial grammar, which is based on propositional types and on the requirement of strict string adjacency between combinable constituents.we show then how, for this formalism, parsing can be efficiently implemented as theorem proving. our approach to encoding types as definite clauses presupposes a modification of standard horn logic syntax to allow internal implications in definite clauses. this modification is needed to account for the types of higher-order functions and, as a consequence, standard prolog-like horn logic theorem proving is not powerful enough. we tackle this problem by adopting an intuitionistic treatment of implication, which has already been proposed elsewhere as an extension of prolog for implementing hypothetical reasoning and modular logic programming.
evaluating a trainable sentence planner for a spoken dialogue system. techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. in this paper we experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. in order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. we show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.
synchronous models of language. in synchronous rewriting, the productions of two rewriting systems are paired and applied synchronously in the derivation of a pair of strings. we present a new synchronous rewriting system and argue that it can handle certain phenomena that are not covered by existing synchronous systems. we also prove some interesting formal/computational properties of our system.
modeling human sentence processing data with a statistical parts-of-speech tagger. it has previously been assumed in the psycholinguistic literature that finite-state models of language are crucially limited in their explanatory power by the locality of the probability distribution and the narrow scope of information used by the model. we show that a simple computational model (a bigram part-of-speech tagger based on the design used by corley and crocker (2000)) makes correct predictions on processing difficulty observed in a wide range of empirical sentence processing data. we use two modes of evaluation: one that relies on comparison with a control sentence, paralleling practice in human studies; another that measures probability drop in the disambiguating region of the sentence. both are surprisingly good indicators of the processing difficulty of garden-path sentences. the sentences tested are drawn from published sources and systematically explore five different types of ambiguity: previous studies have been narrower in scope and smaller in scale. we do not deny the limitations of finite-state models, but argue that our results show that their usefulness has been underestimated.
d-tree grammars. dtg are designed to share some of the advantages of tag while overcoming some of its limitations. dtg involve two composition operations called subsertion and sister-adjunction. the most distinctive feature of dtg is that, unlike tag, there is complete uniformity in the way that the two dtg operations relate lexical items: subsertion always corresponds to complementation and sister-adjunction to modification. furthermore, dtg, unlike tag, can provide a uniform analysis for wh-movement in english and kashmiri, despite the fact that the wh element in kashmiri appears in sentence-second position, and not sentence-initial position as in english.
extraction of tree adjoining grammars from a treebank for korean. we present the implementation of a system which extracts not only lexicalized grammars but also feature-based lexicalized grammars from korean sejong treebank. we report on some practical experiments where we extract tag grammars and tree schemata. above all, full-scale syntactic tags and well-formed morphological analysis in sejong treebank allow us to extract syntactic features. in addition, we modify treebank for extracting lexicalized grammars and convert lexicalized grammars into tree schemata to resolve limited lexical coverage problem of extracted lexicalized grammars.
a three-level model for plan exploration. in modeling the structure of task-related discourse using plans, it is important to distinguish between plans that the agent has adopted and is pursuing and those that are only being considered and explored, since the kinds of utterances arising from a particular domain plan and the patterns of reference to domain plans and movement within the plan tree are quite different in the two cases. this paper presents a three-level discourse model that uses separate domain and exploration layers, in addition to a layer of discourse metaplans, allowing these distinct behavior patterns and the plan adoption and reconsideration moves they imply to be recognized and modeled.
a unification-based semantic interpretation for coordinate constructs. this paper shows that a first-order unification-based semantic interpretation for various coordinate constructs is possible without an explicit use of lambda expressions if we slightly modify the standard montagovian semantics of coordination. this modification, along with partial execution, completely eliminates the lambda reduction steps during semantic interpretation.
meinongian semantics for propositional semantic networks. this paper surveys several approaches to semantic-network semantics that have not previously been treated in the al or computational linguistics literature, though there is a large philosophical literature investigating them in some detail. in particular, propositional semantic networks (exemplihed by sneps) are discussed. it is argued that only a fully intensional ("meinongran") semantics is appropriate for them, and several meinongran systems are presented.
quantifier scope and constituency. traditional approaches to quantifier scope typically need stipulation to exclude readings that are unavailable to human understanders. this paper shows that quantifier scope phenomena can be precisely characterized by a semantic representation constrained by surface constituency, if the distinction between referential and quantificational nps is properly observed. a ccg implementation is described and compared to other approaches.
a practical solution to the problem of automatic part-of-speech induction from text. the problem of part-of-speech induction from text involves two aspects: firstly, a set of word classes is to be derived automatically. secondly, each word of a vocabulary is to be assigned to one or several of these word classes. in this paper we present a method that solves both problems with good accuracy. our approach adopts a mixture of statistical methods that have been successfully applied in word sense induction. its main advantage over previous attempts is that it reduces the syntactic space to only the most important dimensions, thereby almost eliminating the otherwise omnipresent problem of data sparseness.
mapping scrambled korean senteces into english using synchronous tags. synchronous tree adjoining grammars can be used for machine translation. however, translating a free order language such as korean to english is complicated. i present a mechanism to translate scrambled korean sentences into english by combining the concepts of multi-component tags (mc-tags) and synchronous tags (stags).
identifying word translation in non_parallel texts. common algorithms for sentence and word-alignment allow the automatic identification of word translations from parallel texts. this study suggests that the identification of word translations should also be possible with non-parallel and even unrelated texts. the method proposed is based on the assumption that there is a correlation between the patterns of word co-occurrences in texts of different languages.
a finite-state model of human sentence processing. it has previously been assumed in the psycholinguistic literature that finite-state models of language are crucially limited in their explanatory power by the locality of the probability distribution and the narrow scope of information used by the model. we show that a simple computational model (a bigram part-of-speech tagger based on the design used by corley and crocker (2000)) makes correct predictions on processing difficulty observed in a wide range of empirical sentence processing data. we use two modes of evaluation: one that relies on comparison with a control sentence, paralleling practice in human studies; another that measures probability drop in the disambiguating region of the sentence. both are surprisingly good indicators of the processing difficulty of garden-path sentences. the sentences tested are drawn from published sources and systematically explore five different types of ambiguity: previous studies have been narrower in scope and smaller in scale, we do not deny the limitations of finite-state models, but argue that our results show that their usefulness has been underestimated.
automatic identification of word translations from unrelated english and german corpora. algorithms for the alignment of words in translated texts are well established. however, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated texts. this task is more difficult, because most statistical clues useful in the processing of parallel texts cannot be applied to non-parallel texts. whereas for parallel texts in some studies up to 99% of the word alignments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. the current study, which is based on the assumption that there is a correlation between the patterns of word co-occurrences in corpora of different languages, makes a significant improvement to about 72% of word translations identified correctly.
machine aided error-correction environment for korean morphological analysis and part-of-speech tagging. statistical methods require very large corpus with high quality. but building large and faultless annotated corpus is a very difficult job. this paper proposes an efficient method to construct part-of-speech tagged corpus. a rule-based error correction method is proposed to find and correct errors semi-automatically by user-defined rules. we also make use of user's correction log to reflect feedback. experiments were carried out to show the efficiency of error correction process of this workbench. the result shows that about 63.2 % of tagging errors can be corrected.
statistical models for unsupervised prepositional phrase attachement. we present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. it is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. we present results for prepositional phrase attachment in both english and spanish.
text chunking by combining hand-crafted rules and memory-based learning. this paper proposes a hybrid of hand-crafted rules and a machine learning method for chunking korean. in the partially free word-order languages such as korean and japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. thus, the proposed method is primarily based on the rules, and then the residual errors are corrected by adopting a memory-based machine learning method. since the memory-based learning is an efficient method to handle exceptions in natural language processing, it is good at checking whether the estimates are exceptional cases of the rules and revising them. an evaluation of the method yields the improvement in f-score over the rules or various machine learning methods alone.