waashk/dblp huggingface dataset:
using existing systems to supplement small amounts of annotated grammatical relations training data. grammatical relationships (grs) form an important level of natural language processing, but different sets of grs are useful for different purposes. therefore, one may often only have time to obtain a small training corpus with the desired gr annotations. to boost the performance from using such a small training corpus on a transformation rule learner, we use existing systems that find related types of annotations.
some properties of preposition and subordinate conjunction attachments. determining the attachments of prepositions and subordinate conjunctions is a key problem in parsing natural language. this paper presents a trainable approach to making these attachments through transformation sequences and error-driven learning. our approach is broad coverage, and accounts for roughly three times the attachment cases that have previously been handled by corpus-based techniques. in addition, our approach is based on a simplified model of syntax that is more consistent with the practice in current state-of-the-art language processing systems. this paper sketches syntactic and algorithmic details, and presents experimental results on data sets derived from the penn treebank. we obtain an attachment accuracy of 75.4% for the general case, the first such corpus-based result to be reported. for the restricted cases previously studied with corpusbased methods, our approach yields an accuracy comparable to current work (83.1%).
moses: open source toolkit for statistical machine translation. we describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. in addition to the smt decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.
stochastic discourse modeling in spoken dialogue systems using semantic dependency graphs. this investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs. by characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. a statistical approach is adopted to model the relations between words in the user's utterance using the semantic dependency graphs. dependency relation between the headword and other words in a sentence is detected using the semantic dependency grammar. in order to evaluate the proposed method, a dialogue system for medical service is developed. experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. compared with the bayes' classifier and the partial-pattern tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.
tense, aspect and the cognitive representation of time. this paper explores the relationships between a computational theory of temporal representation (as developed by james allen) and a formal linguistic theory of tense (as developed by norbert hornstein) and aspect. it aims to provide explicit answers to four fundamental questions: (1) what is the computational justification for the primitives of a linguistic theory; (2) what is the computational explanation of the formal grammatical constraints: (3) what are the processing constraints imposed on the learnability and markedness of these theoretical constructs: and (4) what are the constraints that a linguistic theory imposes on. representations. we show that one can effectively exploit the interface between the language faculty and the cognitive faculties by using linguistic constraints to determine restructions on the cognitive representations and vice versa.three main results are obtained: (1) we derive an explanation of an observed grammatical constraint on tense .. the linear order constraint .. from the information monotonicity property of the constraint propagation algorithm of allen's temporal system: (2) we formulate a principle of markedness for the basic tense structures based on the computational efficiency of the temporal representations: and (3) we show allen's interval-based temporal system is not arbitrary. but it can be used to explair, independently motivated linguistic constraints on tense and aspect interpretations.we also claim that the methodology of research developed in this study .. "cross-level" investigation of independently motivated formal grammatical theory and computational models .. is a powerful paradigm with which to attack representational problems in basic cognitive domains, e.g., space. time, causality, etc.
automatic discovery of named entity variants: grammar-driven approaches to non-alphabetical transliterations. identification of transliterated names is a particularly difficult task of named entity recognition (ner), especially in the chinese context. of all possible variations of transliterated named entities, the difference between prc and taiwan is the most prevalent and most challenging. in this paper, we introduce a novel approach to the automatic extraction of diverging transliterations of foreign named entities by bootstrapping co-occurrence statistics from tagged and segmented chinese corpus. preliminary experiment yields promising results and shows its potential in nlp applications.
corpus-oriented development of japanese hpsg parsers. this paper reports the corpus-oriented development of a wide-coverage japanese hpsg parser. we first created an hpsg treebank from the edr corpus by using heuristic conversion rules, and then extracted lexical entries from the treebank. the grammar developed using this method attained wide coverage that could hardly be obtained by conventional manual development. we also trained a statistical parser for the grammar on the treebank, and evaluated the parser in terms of the accuracy of semantic-role identification and dependency analysis.
reformatting web documents via header trees. we propose a new method for reformatting web documents by extracting semantic structures from web pages. our approach is to extract trees that describe hierarchical relations in documents. we developed an algorithm for this task by employing the em algorithm and clustering techniques. preliminary experiments showed that our approach was more effective than baseline methods.
evaluation of importance of sentences based on connectivity to title. this paper proposes a method of selecting important sentences from a text based on the evaluation of the connectivity between sentences by using surface information. we assume that the title of a text is the most concise statement which expresses the most essential information of the text, and that the closer a sentence relates to an important sentence, the more important this sentence is. the importance of a sentence is defined as the connectivity between the sentence and the title. the connectivity between two sentences is measured based on correference between a pronoun and a preceding (pro)noun, and on lexical cohesion of lexical items. in an experiment with 80 english texts, which consist of an average of 29.0 sentences, the proposed method has marked recall of 78.2% and precision of 57.7%, with the selection ratio being 25%. the recall and precision values surpass those achieved by conventional methods, which means that our method is more effective in abridging relatively short texts.
annotating and learning compound noun semantics. there is little consensus on a standard experimental design for the compound interpretation task. this paper introduces well-motivated general desiderata for semantic annotation schemes, and describes such a scheme for in-context compound annotation accompanied by detailed publicly available guidelines. classification experiments on an open-text dataset compare favourably with previously reported results and provide a solid baseline for future research.
resume information extraction with cascaded hybrid model. this paper presents an effective approach for resume information extraction to support automatic resume management and routing. a cascaded information extraction (ie) framework is designed. in the first pass, a resume is segmented into a consecutive blocks attached with labels indicating the information types. then in the second pass, the detailed information, such as name and address, are identified in certain blocks (e.g. blocks labelled with personal information), instead of searching globally in the entire resume. the most appropriate model is selected through experiments for each ie task in different passes. the experimental results show that this cascaded hybrid model achieves better f-score than flat models that do not apply the hierarchical structure of resumes. it also shows that applying different ie models in different passes according to the contextual structure is effective.
hal-based cascaded model for variable-length semantic pattern induction from psychiatry web resources. negative life events play an important role in triggering depressive episodes. developing psychiatric services that can automatically identify such events is beneficial for mental health care and prevention. before these services can be provided, some meaningful semantic patterns, such as <lost, parents>, have to be extracted. in this work, we present a text mining framework capable of inducing variable-length semantic patterns from unannotated psychiatry web resources. this framework integrates a cognitive motivated model, hyperspace analog to language (hal), to represent words as well as combinations of words. then, a cascaded induction process (cip) bootstraps with a small set of seed patterns and incorporates relevance feedback to iteratively induce more relevant patterns. the experimental results show that by combining the hal model and relevance feedback, the cip can induce semantic patterns from the unannotated web corpora so as to reduce the reliance on annotated corpora.
boosting statistical machine translation by lemmatization and linear interpolation. data sparseness is one of the factors that degrade statistical machine translation (smt). existing work has shown that using morpho-syntactic information is an effective solution to data sparseness. however, fewer efforts have been made for chinese-to-english smt with using english morpho-syntactic analysis. we found that while english is a language with less inflection, using english lemmas in training can significantly improve the quality of word alignment that leads to yield better translation performance. we carried out comprehensive experiments on multiple training data of varied sizes to prove this. we also proposed a new effective linear interpolation method to integrate multiple homologous features of translation models.
paradigmatic cascades: a linguistically sound model of pronunciation by analogy. we present and experimentally evaluate a new model of prounciation by analogy: the paradigmatic cascades model. given a pronunciation lexicon, this algorithm first extracts the most productive paradigmatic mappings in the graphemic domain, and pairs them statistically with their correlate(s) in the phonemic domain. these mappings are used to search and retrieve in the lexical database the most promising analog of unseen words. we finally apply to the analogs pronunciation the correlated series of mappings in the phonemic domain to get the desired pronunciation.
a transfer model using a typed feature structure rewriting system with inheritance. we propose a model for transfer in machine translation which uses a rewriting system for typed feature structures. the grammar definitions describe transfer relations which are applied on the input structure (a typed feature structure) by the interpreter to produce all possible transfer pairs. the formalism is based on the semantics of typed feature structures as described in [a&iuml;t-kaci 84].
automatic learning of textual entailments with cross-pair similarities. in this paper we define a novel similarity measure between examples of textual entailments and we use it as a kernel function in support vector machines (svms). this allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. the experiments with the data sets of the rte 2005 challenge show an improvement of 4.4% over the state-of-the-art methods.
discovering asymmetric entailment relations between verbs using selectional preferences. in this paper we investigate a novel method to detect asymmetric entailment relations between verbs. our starting point is the idea that some point-wise verb selectional preferences carry relevant semantic information. experiments using word-net as a gold standard show promising results. where applicable, our method, used in combination with other approaches, significantly increases the performance of entailment detection. a combined approach including our model improves the aroc of 5% absolute points with respect to standard models.
memory-based learning: using similarity for smoothing. this paper analyses the relation between the use of similarity in memory-based learning and the notion of backed-off smoothing in statistical language modeling. we show that the two approaches are closely related, and we argue that feature weighting methods in the memory-based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. we report two applications of this approach: pp-attachment and pos-tagging. our method achieves state-of-the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations.
automatic construction of frame representations for spontaneous speech in unrestricted domains. this paper presents a system which automatically generates shallow semantic frame structures for conversational speech in unrestricted domains.we argue that such shallow semantic representations can indeed be generated with a minimum amount of linguistic knowledge engineering and without having to explicitly construct a semantic knowledge base. the system is designed to be robust to deal with the problems of speech dysfluencies, ungrammaticalities, and imperfect speech recognition.initial results on speech transcripts are promising in that correct mappings could be identified in 21% of the clauses of a test set (resp. 44% of this test set where ungrammatical or verb-less clauses were removed).
an implementation of combined partial parser and morphosyntactic disambiguator. the aim of this paper is to present a simple yet efficient implementation of a tool for simultaneous rule-based morphosyntactic tagging and partial parsing formalism. the parser is currently used for creating a tree-bank of partial parses in a valency acquisition project over the ipi pan corpus of polish.
using chunk based partial parsing of spontaneous speech in unrestricted domains for reducing word error rate in speech recognition. in this paper, we present a chunk based partial parsing system for spontaneous, conversational speech in unrestricted domains. we show that the chunk parses produced by this parsing system can be usefully applied to the task of reranking nbest lists from a speech recognizer, using a combination of chunk-based n-gram model scores and chunk coverage scores.the input for the system is nbest lists generated from speech recognizer lattices. the hypotheses from the nbest lists are tagged for part of speech, "cleaned up" by a preprocessing pipe, parsed by a part of speech based chunk parser, and rescored using a backpropagation neural net trained on the chunk based scores. finally, the reranked nbest lists are generated.the results of a system evaluation are promising in that a chunk accuracy of 87.4% is achieved and the best performance on a randomly selected test set is a decrease in world error rate of 0.3 percent (absolute), measured on the new first hypotheses in the reranked nbest lists.
a comparative study on reordering constraints in statistical machine translation. in statistical machine translation, the generation of a translation hypothesis is computationally expensive. if arbitrary word-reorderings are permitted, the search problem is np-hard. on the other hand, if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm.in this paper, we compare two different reordering constraints, namely the itg constraints and the ibm constraints. this comparison includes a theoretical discussion on the permitted number of reorderings for each of these constraints. we show a connection between the itg constraints and the since 1870 known schr&ouml;der numbers.we evaluate these constraints on two tasks: the verbmobil task and the canadian hansards task. the evaluation consists of two parts: first, we check how many of the viterbi alignments of the training corpus satisfy each of these constraints. second, we restrict the search to each of these constraints and compare the resulting translation hypotheses.the experiments will show that the baseline itg constraints are not sufficient on the canadian hansards task. therefore, we present an extension to the itg constraints. these extended itg constraints increase the alignment coverage from about 87% to 96%.
towards a self-extending lexicon. the problem of manually modifying the lexicon appears with any natural language processing program. ideally, a program should be able to acquire new lexical entries from context, the way people learn. we address the problem of acquiring entire phrases, specifically figurative phrases, through augmenting a phrasal lexicon. facilitating such a self-extending lexicon involves (a) disambiguation---selection of the intended phrase from a set of matching phrases, (b) robust parsing---comprehension of partially-matching phrases, and (c) error analysis---use of errors in forming hypotheses about new phrases. we have designed and implemented a program called rina which uses demons to implement functional-grammar principles. rina receives new figurative phrases in context and through the application of a sequence of failure-driven rules, creates and refines both the patterns and the concepts which hold syntactic and semantic information about phrases.
towards a computational treatment of superlatives. i propose a computational treatment of superlatives, starting with superlative constructions and the main challenges in automatically recognising and extracting their components. initial experimental evidence is provided for the value of the proposed work for question answering. i also briefly discuss its potential value for sentiment detection and opinion extraction.
dialect mt: a case study between cantonese and mandarin. machine translation (mt) need not be confined to inter-language activities. in this paper, we discuss inter-dialect mt in general and cantonese-mandarin mt in particular. mandarin and cantonese are two most important dialects of chinese. the former is the national lingua franca and the latter is the most influential dialect in south china, hong kong and overseas. the difference in between is such that mutual intelligibility is impossible. this paper presents, from a computational point of view, a comparative study of mandarin and cantonese at the three aspects of sound systems, grammar rules and vocabulary contents, followed by a discussion of the design and implementation of a dialect mt system between them.
real-time correction of closed-captions. live closed-captions for deaf and hard of hearing audiences are currently produced by stenographers, or by voice writers using speech recognition. both techniques can produce captions with errors. we are currently developing a correction module that allows a user to intercept the real-time caption stream and correct it before it is broadcast. we report results of preliminary experiments on correction rate and actual user performance using a prototype correction module connected to the output of a speech recognition captioning system.
text chunking using regularized winnow. many machine learning methods have recently been applied to natural language processing tasks. among them, the winnow algorithm has been argued to be particularly suitable for nlp problems, due to its robustness to irrelevant features. however in theory, winnow may not converge for non-separable data. to remedy this problem, a modification called regularized winnow has been proposed. in this paper, we apply this new method to text chunking. we show that this method achieves state of the art performance with significantly less computation than previous approaches.
stochastic lexicalized inversion transduction grammar for alignment. we present a version of inversion transduction grammar where rule probabilities are lexicalized throughout the synchronous parse tree, along with pruning techniques for efficient training. alignment results improve over unlexicalized itg on short sentences for which full em is feasible, but pruning seems to have a negative impact on longer sentences.
inducing word alignments with bilexical synchronous trees. this paper compares different bilexical tree-based models for bilingual alignment. em training for the new model benefits from the dynamic programming "hook trick". the model produces improved dependency structure for both languages.
subword-based tagging for confidence-dependent chinese word segmentation. we proposed a subword-based tagging for chinese word segmentation to improve the existing character-based tagging. the subword-based tagging was implemented using the maximum entropy (maxent) and the conditional random fields (crf) methods. we found that the proposed subword-based tagging outperformed the character-based tagging in all comparative experiments. in addition, we proposed a confidence measure approach to combine the results of a dictionary-based and a subword-tagging-based segmentation. this approach can produce an ideal tradeoff between the in-vocaulary rate and out-of-vocabulary rate. our techniques were evaluated using the test data from sighan bakeoff 2005. we achieved higher f-scores than the best results in three of the four corpora: pku(0.951), cityu(0.950) and msr(0.971).
a progressive feature selection algorithm for ultra large feature spaces. recent developments in statistical modeling of various linguistic phenomena have shown that additional features give consistent performance improvements. quite often, improvements are limited by the number of features a system is able to explore. this paper describes a novel progressive training algorithm that selects features from virtually unlimited feature spaces for conditional maximum entropy (cme) modeling. experimental results in edit region identification demonstrate the benefits of the progressive feature selection (pfs) algorithm: the pfs algorithm maintains the same accuracy performance as previous cme feature selection algorithms (e.g., zhou et al., 2003) when the same feature spaces are used. when additional features and their combinations are used, the pfs gives 17.66% relative improvement over the previously reported best result in edit region identification on switchboard corpus (kahn et al., 2005), which leads to a 20% relative error reduction in parsing the switchboard corpus when gold edits are used as the upper bound.
detecting semantic relations between named entities in text using contextual features. this paper proposes a supervised learning method for detecting a semantic relation between a given pair of named entities, which may be located in different sentences. the method employs newly introduced contextual features based on centering theory as well as conventional syntactic and word-based features. these features are organized as a tree structure and are fed into a boosting-based classification algorithm. experimental results show the proposed method outperformed prior methods, and increased precision and recall by 4.4% and 6.7%.