waashk/dblp huggingface dataset:
error profiling: toward a model of english acquisition for deaf learners. in this paper we discuss our approach toward establishing a model of the acquisition of english grammatical structures by users of our english language tutoring system, which has been designed for deaf users of american sign language. we explore the correlation between a corpus of error-tagged texts and their holistic proficiency scores assigned by experts in order to draw initial conclusions about what language errors typically occur at different levels of proficiency in this population. since errors made at lower levels (and not at higher levels) presumably represent constructions acquired before those on which errors are found only at higher levels, this should provide insight into the order of acquisition of english grammatical forms.
language independent extractive summarization. textrank is a system for unsupervised extractive summarization that relies on an innovative application of iterative graph-based ranking algorithms to graphs encoding the cohesive structure of texts. an important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training data, and thus it is highly portable to new languages or domains.
punjabi machine transliteration. machine transliteration is to transcribe a word written in a script with approximate phonetic equivalence in another language. it is useful for machine translation, cross-lingual information retrieval, multilingual text and speech processing. punjabi machine transliteration (pmt) is a special case of machine transliteration and is a process of converting a word from shahmukhi (based on arabic script) to gurmukhi (derivation of landa, shardha and takri, old scripts of indian subcontinent), two scripts of punjabi, irrespective of the type of word.the punjabi machine transliteration system uses transliteration rules (character mappings and dependency rules) for transliteration of shahmukhi words into gurmukhi. the pmt system can transliterate every word written in shahmukhi.
senselearner: word sense disambiguation for all words in unrestricted text. this paper describes senselearner --- a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using wordnet senses. we evaluate the accuracy of senselearner on several standard sense-annotated data sets, and show that it compares favorably with the best results reported during the recent senseval evaluations.
minimum cut model for spoken lecture segmentation. we consider the task of unsupervised lecture segmentation. we formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. our approach moves beyond localized comparisons and takes into account long-range cohesion dependencies. our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.
a method for word sense disambiguation of unrestricted text. selecting the most appropriate sense for an ambiguous word in a sentence is a central problem in natural language processing. in this paper, we present a method that attempts to disambiguate all the nouns, verbs, adverbs and adjectives in a text, using the senses provided in wordnet. the senses are ranked using two sources of information: (1) the internet for gathering statistics for word-word cooccurrences and (2) wordnet for measuring the semantic density for a pair of words. we report an average accuracy of 80% for the first ranked sense, and 91% for the first two ranked senses. extensions of this method for larger windows of more than two words are considered.
universality and individuality: the interaction of noun phrase determiners in corpular clauses. this paper presents an implemented theory for quantifying noun phrases in clauses containing copular verbs (e.g., 'be' and 'become'). proceeding from recent theoretical work by jackendoff [1983], this computational theory recognizes the dependence of the quantification decision on the definiteness, indefiniteness, or classness of both the subject and object of copular verbs in english. jackendoffs intuition about the quantificational interdependence of subject and object has been imported from his broader cognitive theory and reformulated within a constraint propagation framework. extensions reported here include the addition of more active determiners, the expansion of determiner categories, and the treatment of displaced objects. a further finding is that quantificational constraints may propagate across some clausal boundaries. the algorithm is used by the relatus natural language understanding system during a phase of analysis that posts constraints to produce a 'constraint tree.' this phase comes after creation of syntactic deep structure and before sentential reference in a semantic-network model. incorporation of the quantification algorithm in a larger system that parses sentences and builds semantic models from them makes relatus able to acquire taxonomic and identity information from text.
experiences with an on-line translating dialogue system. an english-japanese bi-directional machine translation system was connected to a keyboard conversation function on a workstation, and tested via a satellite link with users in japan and switzerland. the set-up is described, and some informal observations on the nature of the bilingual dialogues reported.
the order of prenominal adjectives in natural language generation. the order of prenominal adjectival modifiers in english is governed by complex and difficult to describe constraints which straddle the boundary between competence and performance. this paper describes and compares a number of statistical and machine learning techniques for ordering sequences of adjectives in the context of a natural language generation system.
unsupervised learning of word-category guessing rules. words unknown to the lexicon present a substantial problem to part-of-speech tagging. in this paper we present a technique for fully unsupervised statistical acquisition of rules which guess possible parts-of-speech for unknown words. three complementary sets of word-guessing rules are induced from the lexicon and a raw corpus: prefix morphological rules, suffix morphological rules and ending-guessing rules. the learning was performed on the brown corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art.
an attributive logic of set descriptions and set operations. this paper provides a model theoretic semantics to feature terms augmented with set descriptions. we provide constraints to specify hpsg style set descriptions, fixed cardinality set descriptions, set-membership constraints, restricted universal role quantifications, set union, intersection, subset and disjointness. a sound, complete and terminating consistency checking procedure is provided to determine the consistency of any given term in the logic. it is shown that determining consistency of terms is a np-complete problem.
feature lattices for maximum entropy modelling. maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building. the iterative scaling algorithm that is used for the parameter estimation is computationally expensive while the feature selection process might require to estimate parameters for many candidate features many times. in this paper we present a novel approach for building maximum entropy models. our approach uses the feature collocation lattice and builds complex candidate features without resorting to iterative scaling.
improving summaries by revising them. this paper describes a program which revises a draft text by aggregating together descriptions of discourse entities, in addition to deleting extraneous information. in contrast to knowledge-rich sentence aggregation approaches explored in the past, this approach exploits statistical parsing and robust coreference detection. in an evaluation involving revision of topic-related summaries using informativeness measures from the tipster summac evaluation, the results show gains in informativeness without compromising readability.
a knowledge-free method for capitalized word disambiguation. in this paper we present an approach to the disambiguation of capitalized words when they are used in the positions where capitalization is expected, such as the first word in a sentence or after a period, quotes, etc.. such words can act as proper names or can be just capitalized variants of common words. the main feature of our approach is that it uses a minimum of prebuilt resources and tires to dynamically infer the disambiguation clues from the entire document. the approach was thoroughly tested and achieved about 98.5% accuracy on unseen texts from the new york times 1996 corpus.
dictionaries of the mind. how lexical information should be formulated, and how it is organized in computer memory for rapid retrieval, are central questions for computational linguists who want to create systems for language understanding. how lexical knowledge is acquired, and how it is organized in human memory for rapid retrieval during language use, are also central questions for cognitive psychologists. some examples of psycholinguistic research on the lexical component of language are reviewed with special attention to their implications for the computational problem.
machine learning of temporal relations. this paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. to address data sparseness, we used temporal reasoning as an over-sampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a maximum entropy classifier on human annotated data. this method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.
the lexical component of natural language processing. of the various problems that natural language processing has revealed, polysemy is probably the most frustrating. people deal with polysemy so easily that potential abiguities are overlooked, whereas computers must work hard to do far less well. a linguistic approach generally involves a parser, a lexicon, and some ad hoc rules for using linguistic context to identify the context-appropriate sense. a statistical approach generally involves the use of word co-occurrence statistics to create a semantic hyperspace where each word, regardless of its polysemy, is represented as a single vector. each approach has strengths and limitations; some combination is often proposed. various possibilities will be discussed in terms of their psychological plausibility. computational linguistics is generally considered to be the branch of engineering that uses computers to do useful things with linguistic signals, but it can also be viewed as an extended test of computational theories of human cognition; it is this latter perspective that psychologists find most interesting. language provides a critical test for the hypothesis that physical symbol systems are adequate to perform all human cognitive functions. as yet, no adequate system for natural language processing has approached human levels of performance.
hidden understanding models of natural language. we describe and evaluate hidden understanding models, a statistical learning approach to natural language understanding. given a string of words, hidden understanding models determine the most likely meaning for the string. we discuss 1) the problem of representing meaning in this framework, 2) the structure of the statistical model, 3) the process of training the model, and 4) the process of understanding using the model. finally, we give experimental results, including results on an arpa evaluation.
a fully statistical approach to natural language interfaces. we present a natural language interface system which is based entirely on trained statistical models. the system consists of three stages of processing: parsing, semantic interpretation, and discourse. each of these stages is modeled as a statistical process. the models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.
multi-field information extraction and cross-document fusion. in this paper, we examine the task of extracting a set of biographic facts about target individuals from a collection of web pages. we automatically annotate training text with positive and negative examples of fact extractions and train rote, na&iuml;ve bayes, and conditional random field extraction models for fact extraction from individual web pages. we then propose and evaluate methods for fusing the extracted information across documents to return a consensus answer. a novel cross-field bootstrapping method leverages data interdependencies to yield improved performance.
the role of centering theory's rough-shift in the teaching and evaluation of writing skills. existing software systems for automated essay scoring can provide nlp researchers with opportunities to test certain theoretical hypotheses, including some derived from centering theory. in this study we employ ets's e-rater essay scoring system to examine whether local discourse coherence, as defined by a measure of rough-shift transitions, might be a significant contributor to the evaluation of essays. our positive results indicate that rough-shifts do indeed capture a source of incoherence, one that has not been closely examined in the centering literature. these results not only justify rough-shifts as a valid transition type, but they also support the original formulation of centering as a measure of discourse continuity even in pronominal-free text.
automatic acquisition of a large subcategorization dictionary from corpora. this paper presents a new method for producing a dictionary of subcategorization frames from unlabelled text corpora. it is shown that statistical filtering of the results of a finite state parser running on the output of a stochastic tagger produces high quality results, despite the error rates of the tagger and the parser. further, it is argued that this method can be used to learn all subcategorization frames, whereas previous methods are not extensible to a general solution to the problem.
distributing representation for robust interpretation of dialogue utterances. a syntax tree or standard semantic representation can be represented as a set of indexed constraints. this paper describes how this idea can be used in task oriented dialogue systems to provide interpretation rules which incorporate structural and contextual constraints where available, and degrade gracefully on ungrammatical input.
chinese work segmentation without using lexicon and hand-crafted training data. chinese word segmentation is the first step in any chinese nlp system. this paper presents a new algorithm for segmenting chinese texts without making use of any lexicon and hand-crafted linguistic resource. the statistical data required by the algorithm, that is, mutual information and the difference of t-score between characters, is derived automatically from raw chinese corpora. the preliminary experiment shows that the segmentation accuracy of our algorithm is acceptable. we hope the gaining of this approach will be beneficial to improving the performance (especially in ability to cope with unknown words and ability to adapt to various domains) of the existing segmenters, though the algorithm itself can also be utilized as a stand-alone segmenter in some nlp applications.
mima search: a structuring knowledge system towards innovation for engineering education. the main aim of the mima (mining information for management and acquisition) search system is to achieve 'structuring knowledge' to accelerate knowledge exploitation in the domains of science and technology. this system integrates natural language processing including ontology development, information retrieval, visualization, and database technology. the 'structuring knowledge' that we define indicates 1) knowledge storage, 2) (hierarchical) classification of knowledge, 3) analysis of knowledge, 4) visualization of knowledge. we aim at integrating different types of databases (papers and patents, technologies and innovations) and knowledge domains, and simultaneously retrieving different types of knowledge. applications for the several targets such as syllabus structuring will also be mentioned.
parsing the lob corpus. this paper presents a rapid and robust parsing system currently used to learn from large bodies of unedited text. the system contains a multivalued part-of-speech disambiguator and a novel parser employing bottom-up recognition to find the constituent phrases of larger structures that might be too difficult to analyze. the results of applying the disambiguator and parser to large sections of the lancaster/oslo-bergen corpus are presented.
simultaneous interpretation utilizing example-based incremental transfer. this paper describes a practical method of automatic simultaneous interpretation utilizing an example-based incremental transfer mechanism. we primarily show how incremental translation is achieved in the context of an example-based framework. we then examine the type of translation examples required for a simultaneous interpretation to create naturally communicative dialogs. finally, we propose a scheme for automatic simultaneous interpretation exploiting this example-based incremental translation mechanism. preliminary experimentation analyzing the performance of our example-based incremental translation mechanism leads us to believe that the proposed scheme can be utilized to achieve a practical simultaneous interpretation system.
acquiring a lexicon from unsegmented speech. we present work-in-progress on the machine acquisition of a lexicon from sentences that are each an unsegmented phone sequence paired with a primitive representation of meaning. a simple exploratory algorithm is described, along with the direction of current work and a discussion of the relevance of the problem for child language acquisition and computer speech recognition.
integrated control of chart items for error repair. this paper describes a system that performs hierarchical error repair for ill-formed sentences, with heterarchical control of chart items produced at the lexical, syntactic, and semantic levels. the system uses an augmented context-free grammar and employs a bidirectional chart parsing algorithm. the system is composed of four subsystems: for lexical, syntactic, surface case, and semantic processing. the subsystems are controlled by an integrated-agenda system. the system employs a parser for well-formed sentences and a second parser for repairing single error sentences. the system ranks possible repairs by penalty scores which are based on both grammar-independent factors (e.g. the significance of the repaired constituent in a local tree) and grammar-independent factors (e.g. error types). this paper focuses on the heterarchical processing of integrated-agenda items (i.e. chart items) at three levels, in the context of single error recovery.
linguistic structure as composition and perturbation. this paper discusses the problem of learning language from unprocessed text and speech signals, concentrating on the problem of learning a lexicon. in particular, it argues for a representation of language in which linguistic parameters like words are built by perturbing a composition of existing parameters. the power of the representation is demonstrated by several examples in text segmentation and compression, acquisition of a lexicon from raw speech, and the acquisition of mappings between text and artificial representations of meaning.
magic for filter optimization in dynamic bottom-up processing. off-line compilation of logic grammars using magic allows an incorporation of filtering into the logic underlying the grammar. the explicit definite clause characterization of filtering resulting from magic compilation allows processor independent and logically clean optimizations of dynamic bottom-up processing with respect to goal-directedness. two filter optimizations based on the program transformation technique of unfolding are discussed which are of practical and theoretical interest.
towards a unified approach to memory- and statistical-based machine translation. we present a set of algorithms that enable us to translate natural language sentences by exploiting both a translation memory and a statistical-based translation model. our results show that an automatically derived translation memory can be used within a statistical framework to often find translations of higher probability than those found using solely a statistical model. the translations produced using both the translation memory and the statistical model are significantly better than translations produced by two commercial systems: our hybrid system translated perfectly 58% of the 505 sentences in a test collection, while the commercial systems translated perfectly only 40-42% of them.
integrating pattern-based and distributional similarity methods for lexical entailment acquisition. this paper addresses the problem of acquiring lexical semantic relationships, applied to the lexical entailment relation. our main contribution is a novel conceptual integration between the two distinct acquisition paradigms for lexical relations - the pattern-based and the distributional similarity approaches. the integrated method exploits mutual complementary information of the two approaches to obtain candidate relations and informative characterizing features. then, a small size training set is used to construct a more accurate supervised classifier, showing significant increase in both recall and precision over the original approaches.
the rhetorical parsing of natural language texts. we derive the rhetorical structures of texts by means of two new, surface-form-based algorithms: one that identifies discourse usages of cue phrases and breaks sentences into clauses, and one that produces valid rhetorical structure trees for unrestricted natural languages texts. the algorithms use information that was derived from a corpus analysis of cue phrases.
robust pronoun resolution with limited knowledge. most traditional approaches to anaphora resolution rely heavily on linguistic and domain knowledge. one of the disadvantages of developing a knowledge-based system, however, is that it is a very labour-intensive and time-consuming task. this paper presents a robust, knowledge-poor approach to resolving pronouns in technical manuals, which operates on texts pre-processed by a part-of-speech tagger. input is checked against agreement and for a number of antecedent indicators. candidates are assigned scores by each indicator and the candidate with the highest score is returned as the antecedent. evaluation reports a success rate of 89.7% which is better than the success rates of the approaches selected for comparison and tested on the same data. in addition, preliminary experiments show that the approach can be successfully adapted for other languages with minimum modifications.
a decision-based approach to rhetorical parsing. we present a shift-reduce rhetorical parsing algorithm that learns to construct rhetorical structures of texts from a corpus of discourse-parse action sequences. the algorithm exploits robust lexical, syntactic, and semantic knowledge sources.
an unsupervised approach to recognizing discourse relations. we present an unsupervised approach to recognizing discourse relations of contrast, explanation-evidence, condition and elaboration that hold between arbitrary spans of texts. we show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.