waashk/dblp huggingface dataset:
automatic acquisition of subcategorization frames from untagged text. this paper describes an implemented program that takes a raw, untagged text corpus as its only input (no open-class dictionary) and generates a partial list of verbs occurring in the text and the subcategorization frames (sfs) in which they occur. verbs are detected by a novel technique based on the case filter of rouvret and vergnaud (1980). the completeness of the output list increases monotonically with the total number of occurrences of each verb in the corpus. false positive rates are one to three percent of observations. five sfs are currently detected and more are planned. ultimately, i expect to provide a large sf dictionary to the nlp community and to train dictionaries for specific corpora.
chinese text segmentation with mbdp-1: making the most of training corpora. this paper describes a system for segmenting chinese text into words using the mbdp-1 algorithm. mbdp-1 is a knowledge-free segmentation algorithm that bootstraps its own lexicon, which starts out empty. experiments on chinese and english corpora show that mbdp-1 reliably outperforms the best previous algorithm when the available hand-segmented training corpus is small. as the size of the hand-segmented training corpus grows, the performance of mbdp-1 converges toward that of the best previous algorithm. the fact that mbdp-1 can be used with a small corpus is expected to be useful not only for the rare event of adapting to a new language, but also for the common event of adapting to a new genre within the same language.
automatic grammar induction and parsing free text: a transformation-based approach. in this paper we describe a new technique for parsing free text: a transformational grammar1 is automatically learned that is capable of accurately parsing text into binary-branching syntactic trees with nonterminals unlabelled. the algorithm works by beginning in a very naive state of knowledge about phrase structure. by repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus, the system learns a set of simple structural transformations that can be applied to reduce error. after describing the algorithm, we present results and compare these results to other recent results in automatic grammar induction.
beyond n-grams: can linguistic sophistication improve language modeling? it seems obvious that a successful model of natural language would incorporate a great deal of both linguistic and world knowledge. interestingly, state of the art language models for speech recognition are based on a very crude linguistic model, namely conditioning the probability of a word on a small fixed number of preceding words. despite many attempts to incorporate more sophisticated information into the models, the n-gram model remains the state of the art, used in virtually all speech recognition systems. in this paper we address the question of whether there is hope in improving language modeling by incorporating more sophisticated linguistic and world knowledge, or whether the n-grams are already capturing the majority of the information that can be employed.
an improved error model for noisy channel spelling correction. the noisy channel model has been applied to a wide range of problems, including spelling correction. these models consist of two components: a source model and a channel model. very little research has gone into improving the channel model for spelling correction. this paper describes a new channel model for spelling correction, based on generic string to string edits. using this model gives significant performance improvements compared to previously proposed models.
man* vs. machine: a case study in base noun phrase learning. a great deal of work has been done demonstrating the ability of machine learning algorithms to automatically extract linguistic knowledge from annotated corpora. very little work has gone into quantifying the difference in ability at this task between a person and a machine. this paper is a first step in that direction.
classifier combination for improved lexical disambiguation. one of the most exciting recent directions in machine learning is the discovery that the combination of multiple classifiers often results in significantly better performance than what can be achieved with a single classifier. in this paper, we first show that the errors made from three different state of the art part of speech taggers are strongly complementary. next, we show how this complementatry behavior can be used to our advantage. by using contextual cues to guide tagger combination, we are able to derive a new tagger that achieves performance significantly greater than any of the individual taggers.
lexical access in connected speech recognition. this paper addresses two issues concerning lexical access in connected speech recognition: 1) the nature of the pre-lexical representation used to initiate lexical lookup 2) the points at which lexical look-up is triggered off this representation. the results of an experiment are reported which was designed to evaluate a number of access strategies proposed in the literature in conjunction with several plausible pre-lexical representations of the speech input. the experiment also extends previous work by utilising a dictionary database containing a realistic rather than illustrative english vocabulary.
co-evolution of language and of the language acquisition device. a new account of parameter setting during grammatical acquisition is presented in terms of generalized categorial grammar embedded in a default inheritance hierarchy, providing a natural partial ordering on the setting of parameters. experiments show that several expermentally effective learners can be defined in this framework. evolutionary simulations suggest that a learner with default initial settings for parameters will emerge, provided that learning is memory limited and the environment of linguistic adaptation contains an appropriate language.
evaluating the accuracy of an unlexicalized statistical parser on the parc depbank. we evaluate the accuracy of an unlexicalized statistical parser, trained on 4k treebanked sentences from balanced data and tested on the parc depbank. we demonstrate that a parser which is competitive in accuracy (without sacrificing processing speed) can be quickly tuned without reliance on large in-domain manually-constructed treebanks. this makes it more practical to use statistical parsers in applications that need access to aspects of predicate-argument structure. the comparison of systems using depbank is not straightforward, so we extend and validate depbank and highlight a number of representation and scoring issues for relational evaluation schemes.
the second release of the rasp system. we describe the new release of the rasp (robust accurate statistical parsing) system, designed for syntactic annotation of free text. the new version includes a revised and more semantically-motivated output representation, an enhanced grammar and part-of-speech tagger lexicon, and a more flexible and semi-supervised training method for the structural parse ranking model. we evaluate the released version on the wsj using a relational evaluation scheme, and describe how the new release allows users to enhance performance using (in-domain) lexical information.
correcting esl errors using phrasal smt techniques. this paper presents a pilot study of the use of phrasal statistical machine translation (smt) techniques to identify and correct writing errors made by learners of english as a second language (esl). using examples of mass noun errors found in the chinese learner error corpus (clec) to guide creation of an engineered training set, we show that application of the smt paradigm can capture errors not well addressed by widely-used proofing tools designed for native speakers. our system was able to correct 61.81% of mistakes in a set of naturally-occurring examples of mass noun errors found on the world wide web, suggesting that efforts to collect alignable corpora of pre- and post-editing esl writing samples offer can enable the development of smt-based writing assistance tools capable of repairing many of the complex syntactic and lexical problems found in the writing of esl learners.
ensemble methods for unsupervised wsd. combination methods are an effective way of improving system performance. this paper examines the benefits of system combination for unsupervised wsd. we investigate several voting- and arbiter-based combination strategies over a diverse pool of unsupervised wsd systems. our combination methods rely on predominant senses which are derived automatically from raw text. experiments using the semcor and senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art.
separating surface order and syntactic relations in a dependency grammar. this paper proposes decoupling the dependency tree from word order, such that surface ordering is not determined by traversing the dependency tree. we develop the notion of a word order domain structure, which is linked but structurally dissimilar to the syntactic dependency tree. the proposal results in a lexicalized, declarative, and formally precise description of word order; features which lack previous proposals for dependency grammars. contrary to other lexicalized approaches to word order, our proposal does not require lexical ambiguities for ordering alternatives.
aligning sentences in parallel corpora. in this paper we describe a statistical technique for aligning sentences with their translations in two parallel corpora. in addition to certain anchor points that are available in our data, the only information about the sentences that we use for calculating alignments is the number of tokens that they contain. because we make no use of the lexical details of the sentence, the alignment computation is fast and therefore practical for application to very large collections of text. we have used this technique to align several million sentences in the english-french hansard corpora and have achieved an accuracy in excess of 99% in a random selected set of 1000 sentence pairs that we checked by hand. we show that even without the benefit of anchor points the correlation between the lengths of aligned sentences is strong enough that we should expect to achieve an accuracy of between 96% and 97%. thus, the technique may be applicable to a wider variety of texts than we have yet tried.
word-sense disambiguation using statistical methods. we describe a statistical technique for assigning senses to words. an instance of a word is assigned a sense by asking a question about the context in which the word appears. the question is constructed to have high mutual information with the translation of that instance in another language. when we incorporated this method of assigning senses into our statistical machine translation system, the error rate of the system decreased by thirteen percent.
word-sense disambiguation using decomposable models. most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. in this paper, a different approach to formulating a probabilistic model is presented along with a case study of the performance of models produced in this manner for the disambiguation of the noun interest. we describe a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, without requiring untested assumptions regarding the form of the model. using this approach, the joint distribution of all variables is described by only the most systematic variable interactions, thereby limiting the number of parameters to be estimated, supporting computational efficiency, and providing an understanding of the data.
the interpretation of relational nouns. this paper decribes a computational treatment of the semantics of relational nouns. it covers relational nouns such as "sister" and "commander", and focuses especially on a particular subcategory of them, called function nouns ("speed", "distance", "rating"). relational nouns are usually viewed as either requiring non-compositional semantic interpretation, or causing an undesirable proliferation of syntactic rules. in contrast to this, we present a treatment which is both syntactically uniform and semantically compositional. the core ideas of this treatment are: (1) the recognition of different levels of semantic analysis; in particular, the distinction between an english-oriented and a domain-oriented level of meaning representation. (2) the analysis of relational nouns as denoting relation-extensions.the paper shows how this approach handles a variety of linguistic constructions involving relational nouns. the treatment presented here has been implemented in bbn's spoken language system, an experimental spoken language interface to a database/graphics system.
terminology finite-state preprocessing for computational lfg. this paper presents a technique to deal with multiword nominal terminology in a computational lexical functional grammar. this method treats multiword terms as single tokens by modifying the preprocessing stage of the grammar (tokenization and morphological analysis), which consists of a cascade of two-level finite-state automata (transducers). we present here how we build the transducers to take terminology into account. we tested the method by parsing a small corpus with the without this treatment of multiword terms. the number of parses and parsing time decrease without affecting the relevance of the results. moreover, the method improves the perspicuity of the analyses.
collective information extraction with relational markov networks. most information extraction (ie) systems treat separate potential extractions as independent. however, in many cases, considering influences between different potential extractions could improve overall accuracy. statistical methods based on undirected graphical models, such as conditional random fields (crfs), have been shown to be an effective approach to learning accurate ie systems. we present a new ie method that employs relational markov networks (a generalization of crfs), which can represent arbitrary dependencies between extractions. this allows for "collective information extraction" that exploits the mutual influence between possible extractions. experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.
named entity scoring for speech input. this paper describes a new scoring algorithm that supports comparison of linguistically annotated data from noisy sources. the new algorithm generalizes the message understanding conference (muc) named entity scoring algorithm, using a comparison based on explicit alignment of the underlying texts, followed by a scoring phase. the scoring procedure maps corresponding tagged regions and compares these according to tag type and tag extent, allowing us to reproduce the muc named entity scoring for identical underlying texts. in addition, the new algorithm scores for content (transcription correctness) of the tagged region, a useful distinction when dealing with noisy data that may differ from a reference transcription (e.g., speech recognizer output). to illustrate the algorithm, we have prepared a small test data set consisting of a careful transcription of speech data and manual insertion of sgml named entity annotation. we report results for this small test corpus on a variety of experiments involving automatic speech recognition and named entity tagging.
automated scoring using a hybrid feature identification technique. this study exploits statistical redundancy inherent in natural language to automatically predict scores for essays. we use a hybrid feature identification method, including syntactic structure analysis, rhetorical structure analysis, and topical analysis, to score essay responses from test-takers of the graduate management admissions test (gmat) and the test of written english (twe). for each essay question, a stepwise linear regression analysis is run on a training set (sample of human scored essay responses) to extract a weighted set of predictive features for each test question. score prediction for cross-validation sets is calculated from the set of predictive features. exact or adjacent agreement between the electronic essay rater (e-rater) score predictions and human rater scores ranged from 87% to 94% across the 15 test questions.
towards automatic classification of discourse elements in essays. educators are interested in essay evaluation systems that include feedback about writing features that can facilitate the essay revision process. for instance, if the thesis statement of a student's essay could be automatically identified, the student could then use this information to reflect on the thesis statement with regard to its quality, and its relationship to other discourse elements in the essay. using a relatively small corpus of manually annotated data, we use bayesian classification to identify thesis statements. this method yields results that are much closer to human performance than the results produced by two baseline systems.
using an on-line dictionary to find rhyming words and pronunciations for unknown words. humans know a great deal about relationships among words. this paper discusses relationships among word pronunciations. we describe a computer system which models human judgement of rhyme by assigning specific roles to the location of primary stress, the similarity of phonetic segments, and other factors. by using the model as an experimental tool, we expect to improve our understanding of rhyme. a related computer model will attempt to generate pronunciations for unknown words by analogy with those for known words. the analogical processes involve techniques for segmenting and matching word spellings, and for mapping spelling to sound in known words. as in the case of rhyme, the computer model will be an important tool for improving our understanding of these processes. both models serve as the basis for functions in the wordsmith automated dictionary system.
adapting an english morphological analyzer for french. a word-based morphological analyzer and a dictionary for recognizing inflected forms of french words have been built by adapting the udict system. we describe the adaptations, emphasizing mechanisms developed to handle french verbs. this work lays the groundwork for doing french derivational morphology and morphology for other languages.
resolving pronominal reference to abstract entities. this paper describes phora, a technique for resolving pronominal reference to either individual or abstract entities. it defines processes for evoking abstract referents from discourse and for resolving both demonstrative and personal pronouns. it successfully interprets 72% of test pronouns, compared to 37% for a leading technique without these features.
a preliminary model of centering in dialog. the centering framework explains local coherence by relating local focus and the form of referring expressions. it has proven useful in monolog, but its utility for multi-party discourse has not been shown, and a variety of issues must be tackled to adapt the model for dialog. this paper reports our application of three naive models of centering theory for dialog. these results will be used as baselines for evaluating future models.
long-distance dependency resolution in automatically acquired wide-coverage pcfg-based lfg approximations. this paper shows how finite approximations of long distance dependency (ldd) resolution can be obtained automatically for wide-coverage, robust, probabilistic lexical-functional grammar (lfg) resources acquired from treebanks. we extract lfg subcategorisation frames and paths linking ldd reentrancies from f-structures generated automatically for the penn-ii treebank trees and use them in an ldd resolution algorithm to parse new text. unlike (collins, 1999; johnson, 2000), in our approach resolution of ldds is done at f-structure (attribute-value structure representations of basic predicate-argument or dependency structure) without empty productions, traces and coindexation in cfg parse trees. currently our best automatically induced grammars achieve 80.97% f-score for f-structures parsing section 23 of the wsj part of the penn-ii treebank and evaluating against the dcu 1051 and 80.24% against the parc 700 dependency bank (king et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (kaplan et al., 2004).
from rags to riches: exploiting the potential of a flexible generation architecture. the rags proposals for generic specification of nlg systems includes a detailed account of data representation, but only an outline view of processing aspects. in this paper we introduce a modular processing architecture with a concrete implementation which aims to meet the rags goals of transparency and reusability. we illustrate the model with the riches system -- a generation system built from simple linguistically-motivated modules.
robust pcfg-based generation using automatically acquired lfg approximations. we present a novel pcfg-based architecture for robust probabilistic generation based on wide-coverage lfg approximations (cahill et al., 2004) automatically extracted from treebanks, maximising the probability of a tree given an f-structure. we evaluate our approach using string-based evaluation. we currently achieve coverage of 95.26%, a bleu score of 0.7227 and string accuracy of 0.7476 on the penn-ii wsj section 23 sentences of length &le;20.
the effect of pitch accenting on pronoun referent resolution. by strictest interpretation, theories of both centering and intonational meaning fail to predict the existence of pitch accented pronominals. yet they occur felicitously in spoken discourse. to explain this, i emphasize the dual functions served by pitch accents, as markers of both propositional (semantic/pragmatic) and attentional salience. this distinction underlies my proposals about the attentional consequences of pitch accents when applied to pronominals, in particular, that while most pitch accents may weaken or reinforce a cospecifier's status as the center of attention, a contrastively stressed pronominal may force a shift, even when contraindicated by textual features.
integrating discourse markers into a pipelined natural language generation architecture. pipelined natural language generation (nlg) systems have grown increasingly complex as architectural modules were added to support language functionalities such as referring expressions, lexical choice, and revision. this has given rise to discussions about the relative placement of these new modules in the overall architecture. recent work on another aspect of multi-paragraph text, discourse markers, indicates it is time to consider where a discourse marker insertion algorithm fits in. we present examples which suggest that in a pipelined nlg architecture, the best approach is to strongly tie it to a revision component. finally, we evaluate the approach in a working multi-page system.
pronominalization in generated discourse and dialogue. previous approaches to pronominalization have largely been theoretical rather than applied in nature. frequently, such methods are based on centering theory, which deals with the resolution of anaphoric pronouns. but it is not clear that complex theoretical mechanisms, while having satisfying explanatory power, are necessary for the actual generation of pronouns. we first illustrate examples of pronouns from various domains, describe a simple method for generating pronouns in an implemented multi-page generation system, and present an evaluation of its performance.