waashk/dblp huggingface dataset:
project april: a progress report. parsing techniques based on rules defining grammaticality are difficult to use with authentic inputs, which are often grammatically messy. instead, the april system seeks a labelled tree structure which maximizes a numerical measure of conformity to statistical norms derived from a sample of parsed text. no distinction between legal and illegal trees arises: any labelled tree has a value. because the search space is large and has an irregular geometry, april seeks the best tree using simulated annealing, a stochastic optimization technique. beginning with an arbitrary tree, many randomly-generated local modifications are considered and adopted or rejected according to their effect on tree-value: acceptance decisions are made probabilistically, subject to a bias against adverse moves which is very weak at the outset but is made to increase as the random walk through the search space continues. this enables the system to converge on the global optimum without getting trapped in local optima. performance of an early version of the april system on authentic inputs is yielding analyses with a mean accuracy of 75.3% using a schedule which increases processing linearly with sentence-length; modifications currently being implemented should eliminate a high proportion of the remaining errors.
domain kernels for word sense disambiguation. in this paper we present a supervised word sense disambiguation methodology, that exploits kernel methods to model sense distinctions. in particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. we defined a kernel function, namely the domain kernel, that allowed us to plug "external knowledge" into the supervised learning process. external knowledge is acquired from unlabeled data in a totally unsupervised way, and it is represented by means of domain models. we evaluated our methodology on several lexical sample tasks in different languages, outperforming significantly the state-of-the-art for each of them, while reducing the amount of labeled training data required for learning.
exploiting comparable corpora and bilingual dictionaries for cross-language text categorization. cross-language text categorization is the task of assigning semantic classes to documents written in a target language (e.g. english) while the system is trained using labeled documents in a source language (e.g. italian).in this work we present many solutions according to the availability of bilingual resources, and we show that it is possible to deal with the problem even when no such resources are accessible. the core technique relies on the automatic acquisition of multilingual domain models from comparable corpora.experiments show the effectiveness of our approach, providing a low cost solution for the cross language text categorization task. in particular, when bilingual dictionaries are available the performance of the categorization gets close to that of monolingual text categorization.
serial combination of rules and statistics: a case study in czech tagging. a hybrid system is described which combines the strength of manual rule-writing and statistical learning, obtaining results superior to both methods if applied separately. the combination of a rule-based system and a statistical one is not parallel but serial: the rule-based system performing partial disambiguation with recall close to 100% is applied first, and a trigram hmm tagger runs on its results. an experiment in czech tagging has been performed with encouraging results.
a computational framework for composition in multiple linguistic domains. we describe a computational framework for a grammar architecture in which different linguistic domains such as morphology, syntax, and semantics are treated not as separate components but compositional domains. the framework is based on combinatory categorial grammars and it uses the morpheme as the basic building block of the categorial lexicon.
topic-focus and salience. most of the current work on corpus annotation is concentrated on morphemics, lexical semantics and sentence structure. however, it becomes more and more obvious that attention should and can be also paid to phenomena that reflect the links between a sentence and its context, i.e. the discourse anchoring of utterances. if conceived in this way, an annotated corpus can be used as a resource for linguistic research not only within the limits of the sentence, but also with regard to discourse patterns. thus, the applications of the research to issues of information retrieval and extraction may be made more effective; also applications in new domains become feasible, be it to serve for inner linguistic (and literary) aims, such as text segmentation, specification of topics of parts of a discourse, or for other disciplines.
lazy unification. unification-based nl parsers that copy argument graphs to prevent their destruction suffer from inefficiency. copying is the most expensive operation in such parsers, and several methods to reduce copying have been devised with varying degrees of success. lazy unification is presented here as a new, conceptually elegant solution that reduces copying by nearly an order of magnitude. lazy unification requires no new slots in the structure of nodes, and only nominal revisions to the unification algorithm.
pcfgs with syntactic and prosodic indicators of speech repairs. a grammatical method of combining two kinds of speech repair cues is presented. one cue, prosodic disjuncture, is detected by a decision tree-based ensemble classifier that uses acoustic cues to identify where normal prosody seems to be interrupted (lickley, 1996). the other cue, syntactic parallelism, codifies the expectation that repairs continue a syntactic category that was left unfinished in the reparandum (levelt, 1983). the two cues are combined in a treebank pcfg whose states are split using a few simple tree transformations. parsing performance on the switchboard and fisher corpora suggests that these two cues help to locate speech repairs in a synergistic way.
an unsupervised model for statistically determining coordinate phrase attachment. this paper examines the use of an unsupervised statistical model for determining the attachment of ambiguous coordinate phrases (cp) of the form <i>n</i>1 <i>p n</i>2 <i>cc n</i>3. the model presented here is based on [ar98], an unsupervised model for determining prepositional phrase attachment. after training on unannotated 1988 wall street journal text, the model performs at 72% accuracy on a development set from sections 14 through 19 of the wsj treebank [msm93].
attention shifting for parsing speech. we present a technique that improves the efficiency of word-lattice parsing as used in speech recognition language modeling. our technique applies a probabilistic parser iteratively where on each iteration it focuses on a different subset of the word-lattice. the parser's attention is shifted towards word-lattice subsets for which there are few or no syntactic analyses posited. this attention-shifting technique provides a six-times increase in speed (measured as the number of parser analyses evaluated) while performing equivalently when used as the first-stage of a multi-stage parsing-based language model.
noun phrase chunking in hebrew: influence of lexical and morphological features. we present a method for noun phrase chunking in hebrew. we show that the traditional definition of base-nps as non-recursive noun phrases does not apply in hebrew, and propose an alternative definition of simple nps. we review syntactic properties of hebrew related to noun phrases, which indicate that the task of hebrew simplenp chunking is harder than base-np chunking in english. as a confirmation, we apply methods known to work well for english to hebrew data. these methods give low results (f from 76 to 86) in hebrew. we then discuss our method, which applies svm induction over lexical and morphological features. morphological features improve the average precision by ~0.5%, recall by ~1%, and f-measure by ~0.75, resulting in a system with average performance of 93% precision, 93.4% recall and 93.2 f-measure.
discriminative classifiers for deterministic dependency parsing. deterministic parsing guided by treebank-induced classifiers has emerged as a simple and efficient alternative to more complex models for data-driven parsing. we present a systematic comparison of memory-based learning (mbl) and support vector machines (svm) for inducing classifiers for deterministic dependency parsing, using data from chinese, english and swedish, together with a variety of different feature models. the comparison shows that svm gives higher accuracy for richly articulated feature models across all languages, albeit with considerably longer training times. the results also confirm that classifier-based deterministic parsing can achieve parsing accuracy very close to the best results reported for more complex parsing models.
combining trigram-based and feature-based methods for context-sensitive spelling correction. this paper addresses the problem of correcting spelling errors that result in valid, though unintended words (such as peace and piece, or quiet and quite) and also the problem of correcting particular word usage errors (such as amount and number, or among and between). such corrections require contextual information and are not handled by conventional spelling programs such as unix spell. first, we introduce a method called trigrams that uses part-of-speech trigrams to encode the context. this method uses a small number of parameters compared to previous methods based on word trigrams. however, it is effectively unable to distinguish among words that have the same part of speech. for this case, an alternative feature-based method called bayes performs better; but bayes is less effective than trigrams when the distinction among words depends on syntactic constraints. a hybrid method called tribayes is then introduced that combines the best of the previous two methods. the improvement in performance of tribayes over its components is verified experimentally. tribayes is also compared with the grammar checker in microsoft word, and is found to have substantially higher performance.
event extraction in a plot advice agent. in this paper we present how the automatic extraction of events from text can be used to both classify narrative texts according to plot quality and produce advice in an interactive learning environment intended to help students with story writing. we focus on the story rewriting task, in which an exemplar story is read to the students and the students rewrite the story in their own words. the system automatically extracts events from the raw text, formalized as a sequence of temporally ordered predicate-arguments. these events are given to a machine-learner that produces a coarse-grained rating of the story. the results of the machine-learner and the extracted events are then used to generate fine-grained advice for the students.
contextual dependencies in unsupervised word segmentation. developing better methods for segmenting continuous text into words is important for improving the processing of asian languages, and may shed light on how humans learn to segment speech. we propose two new bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. the bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. we also show that previous probabilistic models rely crucially on sub-optimal search procedures.
linguistic profiling for authorship recognition and verification. a new technique is introduced, linguistic profiling, in which large numbers of counts of linguistic features are used as a text profile, which can then be compared to average profiles for groups of texts. the technique proves to be quite effective for authorship verification and recognition. the best parameter settings yield a false accept rate of 8.1% at a false reject rate equal to zero for the verification task on a test corpus of student essays, and a 99.4% 2-way recognition accuracy on the same corpus.
building verb predicates: a computational view. a method for the definition of verb predicates is proposed. the definition of the predicates is essentially tied to a semantic interpretation algorithm that determines the predicate for the verb, its semantic roles and adjuncts. as predicate definitions are complete, they can be tested by running the algorithm on some sentences and verifying the resolution of the predicate, semantic roles and adjuncts in those sentences. the predicates are defined semiautomatically with the help of a software environment that uses several sections of a corpus to provide feedback for the definition of the predicates, and then for the subsequent testing and refining of the definitions. the method is very flexible in adding a new predicate to a list of already defined predicates for a given verb. the method builds on an existing approach that defines predicates for wordnet verb classes, and that plans to define predicates for every english verb. the definitions of the predicates and the semantic interpretation algorithm are being used to automatically create a corpus of annotated verb predicates, semantic roles and adjuncts.
improving data driven wordclass tagging by system combination. in this paper we examine how the differences in modelling between different data driven systems performing the same nlp task can be exploited to yield a higher accuracy than the best individual system. we do this by means of an experiment involving the task of morpho-syntactic wordclass tagging. four well-known tagger generator (hidden markov model, memory-based, transformation rules and maximum entropy) are trained on the same corpus data. after comparison, their outputs are combined using several voting strategies and second stage classifiers. all combination taggers outperform their best component, with the best combination showing a 19.1% lower error rate than the best indvidual tagger.
detection of quotations and inserted clauses and its application to dependency structure analysis in spontaneous japanese. japanese dependency structure is usually represented by relationships between phrasal units called bunsetsus. one of the biggest problems with dependency structure analysis in spontaneous speech is that clause boundaries are ambiguous. this paper describes a method for detecting the boundaries of quotations and inserted clauses and that for improving the dependency accuracy by applying the detected boundaries to dependency structure analysis. the quotations and inserted clauses are determined by using an svm-based text chunking method that considers information on morphemes, pauses, fillers, etc. the information on automatically analyzed dependency structure is also used to detect the beginning of the clauses. our evaluation experiment using corpus of spontaneous japanese (csj) showed that the automatically estimated boundaries of quotations and inserted clauses helped to improve the accuracy of dependency structure analysis.
sequential conditional generalized iterative scaling. we describe a speedup for training conditional maximum entropy models. the algorithm is a simple variation on generalized iterative scaling, but converges roughly an order of magnitude faster, depending on the number of constraints, and the way speed is measured. rather than attempting to train all model parameters simultaneously, the algorithm trains them sequentially. the algorithm is easy to implement, typically uses only slightly more memory, and will lead to improvements for most maximum entropy problems.
a step towards the detection of semantic variants of terms in technical documents. this paper reports the results of a preliminary experiment on the detection of semantic variants of terms in a french technical document. the general goal of our work is to help the structuration of terminologies. two kinds of semantic variants can be found in traditional terminologies: strict synonymy links and fuzzier relations like see-also. we have designed three rules which exploit general dictionary information to infer synonymy relations between complex candidate terms. the results have been examined by a human terminologist. the expert has judged that half of the overall pairs of terms are relevant for the semantic variation. he validated an important part of the detected links as synonymy. moreover, it appeared that numerous errors are due to few mis-interpreted links: they could be eliminated by few exception rules.
repairing reference identification failures by relaxation. the goal of this work is the enrichment of human-machine interactions in a natural language environment. we want to provide a framework less restrictive than earlier ones by allowing a speaker leeway in forming an utterance about a task and in determining the conversational vehicle to deliver it. a speaker and listener cannot be assured to have the same beliefs, contexts, backgrounds or goals at each point in a conversation. as a result, difficulties and mistakes arise when a listener interprets a speaker's utterance. these mistakes can lead to various kinds of misunderstandings between speaker and listener, including reference failures or failure to understand the speaker's intention. we call these misunderstandings miscommunication. such mistakes constitute a kind of "ill-formed" input that can slow down and possibly break down communication. our goal is to recognize and isolate such miscommunications and circumvent them. this paper will highlight a particular class of miscommunication - reference problems - by describing a case study, including techniques for avoiding failures of reference.
improving english subcategorization acquisition with diathesis alternations as heuristic information. automatically acquired lexicons with subcategorization information have already proved accurate and useful enough for some purposes but their accuracy still shows room for improvement. by means of diathesis alternation, this paper proposes a new filtering method, which improved the performance of korhonen's acquisition system remarkably, with the precision increased to 91.18% and recall unchanged, making the acquired lexicon much more practical for further manual proofreading and other nlp uses.
parsing algorithms and metrics. many different metrics exist for evaluating parsing results, including viterbi, crossing brackets rate, zero crossing brackets rate, and several others. however, most parsing algorithms, including the viterbi algorithm, attempt to optimize the same metric, namely the probability of getting the correct labelled tree. by choosing a parsing algorithm appropriate for the evaluation metric, better performance can be achieved. we present two new algorithms: the "labelled recall algorithm," which maximizes the expected labelled recall rate, and the "bracketed recall algorithm," which maximizes the bracketed recall rate. experimental results are given, showing that the two new algorithms have improved performance over the viterbi algorithm on many criteria, especially the ones that they optimize.
an application of wordnet to prepositional attachment. this paper presents a method for word sense disambiguation and coherence understanding of prepositional relations. the method relies on information provided by wordnet 1.5. we first classify prepositional attachments according to semantic equivalence of phrase heads and then apply inferential heuristics for understanding the validity of prepositional structures.
recognizing expressions of commonsense psychology in english text. many applications of natural language processing technologies involve analyzing texts that concern the psychological states and processes of people, including their beliefs, goals, predictions, explanations, and plans. in this paper, we describe our efforts to create a robust, large-scale lexical-semantic resource for the recognition and classification of expressions of commonsense psychology in english text. we achieve high levels of precision and recall by hand-authoring sets of local grammars for commonsense psychology concepts, and show that this approach can achieve classification performance greater than that obtained by using machine learning techniques. we demonstrate the utility of this resource for large-scale corpus analysis by identifying references to adversarial and competitive goals in political speeches throughout u.s. history.
methods for using textual entailment in open-domain question answering. work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. in this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (q/a) systems. in our experiments, we show that when textual entailment information is used to either filter or rank answers returned by a q/a system, accuracy can be increased by as much as 20% overall.
scaling distributional similarity to large corpora. accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. however, the na&iuml;ve nearest-neighbour approach to comparing context vectors extracted from large corpora scales poorly (o(n2) in the vocabulary size).in this paper, we compare several existing approaches to approximating the nearest-neighbour search for distributional similarity. we investigate the trade-off between efficiency and accuracy, and find that sash (houle and sakuma, 2005) provides the best balance.
experiments with interactive question-answering. this paper describes a novel framework for interactive question-answering (q/a) based on predictive questioning. generated off-line from topic representations of complex scenarios, predictive questions represent requests for information that capture the most salient (and diverse) aspects of a topic. we present experimental results from large user studies (featuring a fully-implemented interactive q/a system named ferret) that demonstrates that surprising performance is achieved by integrating predictive questions into the context of a q/a dialogue.
the role of lexico-semantic feedback in open-domain textual question-answering. this paper presents an open-domain textual question-answering system that uses several feedback loops to enhance its performance. these feedback loops combine in a new way statistical results with syntactic, semantic or pragmatic information derived from texts and lexical databases. the paper presents the contribution of each feedback loop to the overall performance of 76% human-assessed precise answers.
interleaving universal principles and relational constraints over typed feature logic. we introduce a typed feature logic system providing both universal implicational principles as well as definite clauses over feature terms. we show that such an architecture supports a modular encoding of linguistic theories and allows for a compact representation using underspecification. the system is fully implemented and has been used as a workbench to develop and test large hpsg grammars. the techniques described in this paper are not restricted to a specific implementation, but could be added to many current feature-based grammar development systems.
an efficient parsing algorithm for tree adjoining grammars. in the literature, tree adjoining grammars (tags) are propagated to be adequate for natural language description --- analysis as well as generation. in this paper we concentrate on the direction of analysis. especially important for an implementation of that task is how efficiently this can be done, i.e., how readily the word problem can be solved for tags. up to now, a parser with o(n6) steps in the worst case was known where n is the length of the input string. in this paper, the result is improved to o(n4 log n) as a new lowest upper bound. the paper demonstrates how local interpretion of tag trees allows this reduction.