waashk/dblp huggingface dataset:
ranking text units according to textual saliency, connectivity and topic aptness. an efficient use of lexical cohesion is described for ranking text units according to their contribution in defining the meaning of a text (textual saliency), their ability to form a cohesive subtext (textual connectivity) and the extent and effectiveness to which they address the different topics which characterize the subject matter of the text (topic aptness). a specific application is also discussed where the method described is employed to build the indexing component of a summarization system to provide both generic and query-based indicative summaries.
ordering among premodifiers. we present a corpus-based study of the sequential ordering among premodifiers in noun phrases. this information is important for the fluency of generated text in practical applications. we propose and evaluate three approaches to identify sequential order among premodifiers: direct evidence, transitive closure, and clustering. our implemented system can make over 94% of such ordering decisions correctly, as evaluated on a large, previously unseen test corpus.
on the automatic transformation of class membership criteria. this paper addresses a problem that may arise in classification tasks: the design of procedures for matching an instance with a set of criteria for class membership in such a way as to permit the intelligent handling of inexact, as well as exact matches. an inexact match is a comparison between an instance and a set of criteria (or a second instance) which has the result that some, but not all, of the criteria described (or exemplified) in the second are found to be satisfied in the first. an exact match is such a comparison for which all of the criteria of the second are found to be satisfied in the first. the approach presented in this paper is to transform the set of criteria for class membership into an exemplary instance of a member of the class, which exhibits a set of characteristics whose presence is necessary and sufficient for membership in that class. use of this exemplary instance during the matching process appears to permit important functions associated with inexact matching to be easily performed, and also to have a beneficial effect on the overall efficiency of the matching process.
a snow based supertagger with application to np chunking. supertagging is the tagging process of assigning the correct elementary tree of ltag, or the correct supertag, to each word of an input sentence. in this paper we propose to use supertags to expose syntactic dependencies which are unavailable with pos tags. we first propose a novel method of applying sparse network of winnow (snow) to sequential models. then we use it to construct a supertagger that uses long distance syntactical dependencies, and the supertagger achieves an accuracy of 92.41%. we apply the supertagger to np chunking. the use of supertags in np chunking gives rise to almost 1% absolute increase (from 92.03% to 92.95%) in f-score under transformation based learning(tbl) frame. the surpertagger described here provides an effective and efficient way to exploit syntactic information.
the production of code-mixed discourse. we propose a comprehensive theory of code-mixed discourse, encompassing equivalencepoint and insertional code-switching, palindromic constructions and lexical borrowing. the starting point is a production model of code-switching acconting for empirical observations about switch-point distribution (the equivalence constraint), well-formedness of monolingual fragments, conservation of constituent structure and lack of constraint between successive switch points, without invoking any "code-switching grammar". code-switched sentence production makes alternate reference to two virtual monolingual sentences, one in each language, and is based on conservative conditions on language labeling of constituents, together with a constraint against real-time "look-ahead" from one code-switch to the next. selective weakening of model conditions can produce (i) the type of palindromic (or portmanteau) construction occasionally occurring e.g., in switches between prepositional and postpositional languages, (ii) the switching by "insertion" of very specific kinds of constituent reported e.g., for french noun phrases in switching with arabic and, most important, (iii) lexical borrowing. borrowing can create ambiguity as to language membership of sentence items, but the model predicts where this can be resolved, and the confirmation of these predictions, based on empirical studies of inflectional morphology, validates key aspects of the model.
exploring correlation of dependency relation paths for answer extraction. in this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. the correlations are further incorporated into a maximum entropy-based ranking model which estimates path weights from training. experimental results show that our method significantly outperforms state-of-the-art syntactic relation-based methods by up to 20% in mrr.
implementing a characterization of genre for automatic genre identification of web pages. in this paper, we propose an implementable characterization of genre suitable for automatic genre identification of web pages. this characterization is implemented as an inferential model based on a modified version of bayes' theorem. such a model can deal with genre hybridism and individualization, two important forces behind genre evolution. results show that this approach is effective and is worth further research.
adding syntax to dynamic programming for aligning comparable texts for the generation of paraphrases. multiple sequence alignment techniques have recently gained popularity in the natural language community, especially for tasks such as machine translation, text generation, and paraphrase identification. prior work falls into two categories, depending on the type of input used: (a) parallel corpora (e.g., multiple translations of the same text) or (b) comparable texts (non-parallel but on the same topic). so far, only techniques based on parallel texts have successfully used syntactic information to guide alignments. in this paper, we describe an algorithm for incorporating syntactic features in the alignment process for non-parallel texts with the goal of generating novel paraphrases of existing texts. our method uses dynamic programming with alignment decision based on the local syntactic similarity between two sentences. our results show that syntactic alignment outrivals syntax-free methods by 20% in both grammaticality and fidelity when computed over the novel sentences generated by alignment-induced finite state automata.
multi-criteria-based active learning for named entity recognition. in this paper, we propose a multi-criteria-based active learning approach and effectively apply it to named entity recognition. active learning targets to minimize the human annotation efforts by selecting examples for labeling. to maximize the contribution of the selected examples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quantify them. more comprehensively, we incorporate all the criteria using two selection strategies, both of which result in less labeling cost than single-criterion-based method. the results of the named entity recognition in both muc-6 and genia show that the labeling cost can be reduced by at least 80% without degrading the performance.
splitting complex temporal questions for question answering systems. this paper presents a multi-layered question answering (q.a.) architecture suitable for enhancing current q.a. capabilities with the possibility of processing complex questions. that is, questions whose answer needs to be gathered from pieces of factual information scattered in different documents. specifically, we have designed a layer oriented to process the different types of temporal questions. complex temporal questions are first decomposed into simpler ones, according to the temporal relationships expressed in the original question.in the same way, the answers of each simple question are re-composed, fulfilling the temporal restrictions of the original complex question.using this architecture, a temporal q.a. system has been developed.in this paper, we focus on explaining the first part of the process: the decomposition of the complex questions. furthermore, it has been evaluated with the terqas question corpus of 112 temporal questions. for the task of question splitting our system has performed, in terms of precision and recall, 85% and 71%, respectively.
a dom tree alignment model for mining parallel data from the web. this paper presents a new web mining scheme for parallel data acquisition. based on the document object model (dom), a web page is represented as a dom tree. then a dom tree alignment model is proposed to identify the translationally equivalent texts and hyperlinks between two parallel dom trees. by tracing the identified parallel hyperlinks, parallel web documents are recursively mined. compared with previous mining schemes, the benchmarks show that this new mining scheme improves the mining coverage, reduces mining bandwidth, and enhances the quality of mined parallel sentences.
unsupervised topic identification by integrating linguistic and visual information based on hidden markov models. this paper presents an unsupervised topic identification method integrating linguistic and visual information based on hidden markov models (hmms). we employ hmms for topic identification, wherein a state corresponds to a topic and various features including linguistic, visual and audio information are observed. our experiments on two kinds of cooking tv programs show the effectiveness of our proposed method.
extending kimmo's two-level model of morphology. this paper describes the problems faced while using kimmo's two-level model to describe certain indian languages such as tamil and hindi. the two-level model is shown to be descriptively inadequate to address these problems. a simple extension to the basic two-level model is introduced which allows conflicting phonological rules to coexist. the computational complexity of the extension is the same as kimmo's two-level model.
using restriction to extend parsing algorithms for complex-feature-based formalisms. grammar formalisms based on the encoding of grammatical information in complex-valued feature systems enjoy some currency both in linguistics and natural-language-processing research. such formalisms can be thought of by analogy to context-free grammars as generalizing the notion of nonterminal symbol from a finite domain of atomic elements to a possibly infinite domain of directed graph structures of a certain sort. unfortunately, in moving to an infinite nonterminal domain, standard methods of parsing may no longer be applicable to the formalism. typically, the problem manifests itself as gross inefficiency or even nontermination of the algorithms. in this paper, we discuss a solution to the problem of extending parsing algorithms to formalisms with possibly infinite nonterminal domains, a solution based on a general technique we call restriction. as a particular example of such an extension, we present a complete, correct, terminating extension of earley's algorithm that uses restriction to perform top-down filtering. our implementation of this algorithm demonstrates the drastic elimination of chart edges that can be achieved by this technique. finally, we describe further uses for the technique---including parsing other grammar formalisms, including definite-clause grammars; extending other parsing algorithms, including lr methods and syntactic preference modeling algorithms; and efficient indexing.
incremental parser generation for tree adjoining grammars. this paper describes the incremental generation of parse tables for the lr-type parsing of tree adjoining languages (tals). the algorithm presented handles modifications to the input grammar by updating the parser generated so far. in this paper, a lazy generation of lr-type parsers for tals is defined in which parse tables are created by need while parsing. we then describe an incremental parser generator for tals which responds to modification of the input grammar by updating parse tables built so far.
a semantic-head-driven generation algorithm for unification-based formalisms. we present an algorithm for generating strings from logical form encodings that improves upon previous algorithms in that it places fewer restrictions on the class of grammars to which it is applicable. in particular, unlike an earley deduction generator (shieber, 1988), it allows use of semantically nonmonotonic grammars, yet unlike topdown methods, it also permits left-recursion. the enabling design feature of the algorithm is its implicit traversal of the analysis tree for the string being generated in a semantic-head-driven fashion.
conditions on consistency of probabilistic tree adjoining grammars. much of the power of probabilistic methods in modelling language comes from their ability to compare several derivations for the same string in the language. an important starting point for the study of such cross-derivational properties is the notion of consistency. the probability model defined by a probabilistic grammar is said to be consistent if the probabilities assigned to all the strings in the language sum to one. from the literature on probabilistic context-free grammars (cfgs), we know precisely the conditions which ensure that consistency is true for a given cfg. this paper derives the conditions under which a given probabilistic tree adjoining grammar (tag) can be shown to be consistent. it gives a simple algorithm for checking consistency and gives the formal justification for its correctness. the conditions derived here can be used to ensure that probability models that use tags can be checked for deficiency (i.e. whether any probability mass is assigned to strings that cannot be generated).
semantic discourse segmentation and labeling for route instructions. in order to build a simulated robot that accepts instructions in unconstrained natural language, a corpus of 427 route instructions was collected from human subjects in the office navigation domain. the instructions were segmented by the steps in the actual route and labeled with the action taken in each step. this flat formulation reduced the problem to an ie/segmentation task, to which we applied conditional random fields. we compared the performance of crfs with a set of hand-written rules. the result showed that crfs perform better with a 73.7% success rate.
question answering as question-biased term extraction: a new approach toward multilingual qa. this paper regards question answering (qa) as question-biased term extraction (qbte). this new qbte approach liberates qa systems from the heavy burden imposed by question types (or answer types). in conventional approaches, a qa system analyzes a given question and determines the question type, and then it selects answers from among answer candidates that match the question type. consequently, the output of a qa system is restricted by the design of the question types. the qbte directly extracts answers as terms biased by the question. to confirm the feasibility of our qbte approach, we conducted experiments on the crl qa data based on 10-fold cross validation, using maximum entropy models (mems) as an ml technique. experimental results showed that the trained system achieved 0.36 in mrr and 0.47 in top5 accuracy.
exact decoding for jointly labeling and chunking sequences. there are two decoding algorithms essential to the area of natural language processing. one is the viterbi algorithm for linear-chain models, such as hmms or crfs. the other is the cky algorithm for probabilistic context free grammars. however, tasks such as noun phrase chunking and relation extraction seem to fall between the two, neither of them being the best fit. ideally we would like to model entities and relations, with two layers of labels. we present a tractable algorithm for exact inference over two layers of labels and chunks with time complexity o(n2), and provide empirical results comparing our model with linear-chain models.
an empirical study of active learning with support vector machines for japanese word segmentation. we explore how active learning with support vector machines works well for a non-trivial task in natural language processing. we use japanese word segmentation as a test case. in particular, we discuss how the size of a pool affects the learning curve. it is found that in the early stage of training with a larger pool, more labeled examples are required to achieve a given level of accuracy than those with a smaller pool. in addition, we propose a novel technique to use a large number of unlabeled examples effectively by adding them gradually to a pool. the experimental results show that our technique requires less labeled examples than those with the technique in previous research. to achieve 97.0% accuracy, the proposed technique needs 59.3% of labeled examples that are required when using the previous technique and only 17.4% of labeled examples with random sampling.
retrieving collocations by co-occurrences and word order constraints. in this paper, we describe a method for automatically retrieving collocations from large text corpora. this method retrieve collocations in the following stages: 1) extracting strings of characters as units of collocations 2) extracting recurrent combinations of strings in accordance with their word order in a corpus as collocations. through the method, various range of collocations, especially domain specific collocations, are retrieved. the method is practical because it uses plain texts without any information dependent on a language such as lexical knowledge and parts of speech.
translating hpsg-style outputs of a robust parser into typed dynamic logic. the present paper proposes a method by which to translate outputs of a robust hpsg parser into semantic representations of typed dynamic logic (tdl), a dynamic plural semantics defined in typed lambda calculus. with its higher-order representations of contexts, tdl analyzes and describes the inherently inter-sentential nature of quantification and anaphora in a strictly lexicalized and compositional manner. the present study shows that the proposed translation method successfully combines robustness and descriptive adequacy of contemporary semantics. the present implementation achieves high coverage, approximately 90%, for the real text of the penn treebank corpus.
compiling a lexicon of cooking actions for animation generation. this paper describes a system which generates animations for cooking actions in recipes, to help people understand recipes written in japanese. the major goal of this research is to increase the scalability of the system, i.e., to develop a system which can handle various kinds of cooking actions. we designed and compiled the lexicon of cooking actions required for the animation generation system. the lexicon includes the action plan used for animation generation, and the information about ingredients upon which the cooking action is taken. preliminary evaluation shows that our lexicon contains most of the cooking actions that appear in japanese recipes. we also discuss how to handle linguistic expressions in recipes, which are not included in the lexicon, in order to generate animations for them.
maximum entropy model learning of the translation rules. this paper proposes a learning method of translation rules from parallel corpora. this method applies the maximum entropy principle to a probabilistic model of translation rules. first, we define feature functions which express statistical properties of this model. next, in order to optimize the model, the system iterates following steps: (1) selects a feature function which maximizes loglikelihood, and (2) adds this function to the model incrementally. as computational cost associated with this model is too expensive, we propose several methods to suppress the overhead in order to realize the system. the result shows that it attained 69.54% recall rate.
natural vs. precise concise languages for human operation of computers: research issues and experimental approaches. this paper raises concerns that natural language front ends for computer systems can limit a researcher's scope of thinking, yield inappropriately complex systems, and exaggerate public fear of computers. alternative modes of computer use are suggested and the role of psychologically oriented controlled experimentation is emphasized. research methods and recent experimental results are briefly reviewed.
recognition of linear context-free rewriting systems. the class of linear context-free rewriting systems has been introduced as a generalization of a class of grammar formalisms known as mildly context-sensitive. the recognition problem for linear context-free rewriting languages is studied at length here, presenting evidence that, even in some restricted cases, it cannot be solved efficiently. this entails the existence of a gap between, for example, tree adjoining languages and the subclass of linear context-free rewriting languages that generalizes the former class; such a gap is attributed to "crossing configurations". a few other interesting consequences of the main result are discussed, that concern the recognition problem for linear context-free rewriting languages.
generating referring expressions in open domains. we present an algorithm for generating referring expressions in open domains. existing algorithms work at the semantic level and assume the availability of a classification for attributes, which is only feasible for restricted domains. our alternative works at the realisation level, relies on word-net synonym and antonym sets, and gives equivalent results on the examples cited in the literature and improved results for examples that prior approaches cannot handle. we believe that ours is also the first algorithm that allows for the incremental incorporation of relations. we present a novel corpus-evaluation using referring expressions from the penn wall street journal treebank.
efficient transformation-based parsing. in transformation-based parsing, a finite sequence of tree rewriting rules are checked for application to an input structure. since in practice only a small percentage of rules are applied to any particular structure, the naive parsing algorithm is rather inefficient. we exploit this sparseness in rule applications to derive an algorithm two to three orders of magnitude faster than the standard parsing algorithm.
string transformation learning. string transformation systems have been introduced in (brill, 1995) and have several applications in natural language processing. in this work we consider the computational problem of automatically learning from a given corpus the set of transformations presenting the best evidence. we introduce an original data structure and efficient algorithms that learn some families of transformations that are relevant for part-of-speech tagging and phonological rule systems. we also show that the same learning problem becomes np-hard in cases of an unbounded use of don't care symbols in a transformation.
corpus-based linguistic indicators for aspectual classification. fourteen indicators that measure the frequency of lexico-syntactic phenomena linguistically related to aspectual class are applied to aspectual classification. this group of indicators is shown to improve classification performance for two aspectual distinctions, stativity and completedness (i.e., telicity), over unrestricted sets of verbs from two corpora. several of these indicators have not previously been discovered to correlate with aspect.