waashk/dblp huggingface dataset:
exploiting constituent dependencies for tree kernel-based semantic relation extraction. this paper proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction. it exploits constituent dependencies to keep the nodes and their head children along the path connecting the two entities, while removing the noisy information from the syntactic parse tree, eventually leading to a dynamic syntactic parse tree. this paper also explores entity features and their combined features in a unified parse and semantic tree, which integrates both structured syntactic parse information and entity-related semantic information. evaluation on the ace rdc 2004 corpus shows that our dynamic syntactic parse tree outperforms all previous tree spans, and the composite kernel combining this tree kernel with a linear state-of-the-art feature-based kernel, achieves the so far best performance.
normalizing sms: are two metaphors better than one ? electronic written texts used in computermediated interactions (e-mails, blogs, chats, etc) present major deviations from the norm of the language. this paper presents an comparative study of systems aiming at normalizing the orthography of french sms messages: after discussing the linguistic peculiarities of these messages, and possible approaches to their automatic normalization, we present, evaluate and contrast two systems, one drawing inspiration from the machine translation task; the other using techniques that are commonly used in automatic speech recognition devices. combining both approaches, our best normalization system achieves about 11% word error rate on a test set of about 3000 unseen messages.
parsing the syntagrus treebank of russian. we present the first results on parsing the syntagrus treebank of russian with a data-driven dependency parser, achieving a labeled attachment score of over 82% and an unlabeled attachment score of 89%. a feature analysis shows that high parsing accuracy is crucially dependent on the use of both lexical and morphological features. we conjecture that the latter result can be generalized to richly inflected languages in general, provided that sufficient amounts of training data are available.
an improved hierarchical bayesian model of language for document classification. this paper addresses the fundamental problem of document classification, and we focus attention on classification problems where the classes are mutually exclusive. in the course of the paper we advocate an approximate sampling distribution for word counts in documents, and demonstrate the model's capacity to outperform both the simple multinomial and more recently proposed extensions on the classification task. we also compare the classifiers to a linear svm, and show that provided certain conditions are met, the new model allows performance which exceeds that of the svm and attains amongst the very best published results on the newsgroups classification task.
a supervised algorithm for verb disambiguation into verbnet classes. verbnet (vn) is a major large-scale english verb lexicon. mapping verb instances to their vn classes has been proven useful for several nlp tasks. however, verbs are polysemous with respect to their vn classes. we introduce a novel supervised learning model for mapping verb instances to vn classes, using rich syntactic features and class membership constraints. we evaluate the algorithm in both in-domain and corpus adaptation scenarios. in both cases, we use the manually tagged semlink wsj corpus as training data. for indomain (testing on semlink wsj data), we achieve 95.9% accuracy, 35.1% error reduction (er) over a strong baseline. for adaptation, we test on the genia corpus and achieve 72.4% accuracy with 10.7% er. this is the first large-scale experimentation with automatic algorithms for this task.
textual demand analysis: detection of users' wants and needs from opinions. this paper tackles textual demand analysis, the task of capturing what people want or need, rather than identifying what they like or dislike, on which much conventional work has focused. it exploits syntactic patterns as clues to detect previously unknown demands, and requires domaindependent knowledge to get high recall. to build such patterns we created an unsupervised pattern induction method relying on the hypothesis that there are commonly desired aspects throughout a domain corpus. experimental results show that the proposed method detects twice to four times as many demand expressions in japanese discussion forums compared to a baseline method.
regenerating hypotheses for statistical machine translation. this paper studies three techniques that improve the quality of n-best hypotheses through additional regeneration process. unlike the multi-system consensus approach where multiple translation systems are used, our improvement is achieved through the expansion of the n-best hypotheses from a single system. we explore three different methods to implement the regeneration process: redecoding, n-gram expansion, and confusion network-based regeneration. experiments on chinese-to-english nist and iwslt tasks show that all three methods obtain consistent improvements. moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.81 bleu-score on iwslt'06, 0.57 on nist'03, 0.61 on nist'05 test set respectively.
latent morpho-semantic analysis: multilingual information retrieval with character n-grams and mutual information. we describe an entirely statistics-based, unsupervised, and language-independent approach to multilingual information retrieval, which we call latent morpho-semantic analysis (lmsa). lmsa overcomes some of the shortcomings of related previous approaches such as latent semantic analysis (lsa). lmsa has an important theoretical advantage over lsa: it combines well-known techniques in a novel way to break the terms of lsa down into units which correspond more closely to morphemes. thus, it has a particular appeal for use with morphologically complex languages such as arabic. we show through empirical results that the theoretical advantages of lmsa can translate into significant gains in precision in multilingual information retrieval tests. these gains are not matched either when a standard stemmer is used with lsa, or when terms are indiscriminately broken down into n-grams.
investigating the portability of corpus-derived cue phrases for dialogue act classification. we present recent work in the area of cross-domain dialogue act tagging. our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features - principally involving word n-gram cue phrases. we apply automatically extracted cues from one corpus to a new annotated data set, to determine the portability and generality of the cues we learn. we show that our automatically acquired cues are general enough to serve as a cross-domain classification mechanism.
the choice of features for classification of verbs in biomedical texts. we conduct large-scale experiments to investigate optimal features for classification of verbs in biomedical texts. we introduce a range of feature sets and associated extraction techniques, and evaluate them thoroughly using a robust method new to the task: cost-based framework for pairwise clustering. our best results compare favourably with earlier ones. interestingly, they are obtained with sophisticated feature sets which include lexical and semantic information about selectional preferences of verbs. the latter are acquired automatically from corpus data using a fully unsupervised method.
almost flat functional semantics for speech translation. we introduce a novel semantic representation formalism, almost flat functional semantics (aff), which is designed as an intelligent compromise between linguistically motivated predicate/argument semantics and ad hoc engineering solutions based on flat feature/value lists; the central idea is to tag each semantic element with the functional marking which most closely surrounds it. we argue that aff is well-suited for medium-vocabulary speech translation applications, and describe simple and general algorithms for parsing, generating and performing transfer using aff representations. the formalism has been fully implemented within a medium-vocabulary interlingua-based open source speech translation system which translates between english, french, japanese and arabic.
contents modelling of neo-sumerian ur iii economic text corpus. this paper describes a system for processing economic documents written in the ancient sumerian language. the system is application-oriented and takes advantage of the simplicity of ancient economy. we have developed an ontology for a selected branch of economic activities. we translate the documents into a meaning representation language by means of a semantic grammar. the meaning representation language is constructed in a way that allows us to handle massive ambiguity caused by: the specifics of the sumerian writing system (signs' polyvalence, lack of mid-word signs), our incomplete knowledge of the sumerian language and frequent damages of documents. the system is augmented with the capability of processing documents whose parts describe concepts not included in the ontology and grammar. as an effect we obtain a structural description of the documents contents in the meaning representation language, ready to use in historical research.
two-phased event relation acquisition: coupling the relation-oriented and argument-oriented approaches. addressing the task of acquiring semantic relations between events from a large corpus, we first argue the complementarity between the pattern-based relation-oriented approach and the anchor-based argument-oriented approach. we then propose a two-phased approach, which first uses lexico-syntactic patterns to acquire predicate pairs and then uses two types of anchors to identify shared arguments. the present results of our empirical evaluation on a large-scale japanese web corpus have shown that (a) the anchor-based filtering extensively improves the accuracy of predicate pair acquisition, (b) the two types of anchors are almost equally contributive and combining them improves recall without losing accuracy, and (c) the anchor-based method also achieves high accuracy in shared argument identification.
grammar comparison study for translational equivalence modeling and statistical machine translation. this paper presents a general platform, namely synchronous tree sequence substitution grammar (stssg), for the grammar comparison study in translational equivalence modeling (tem) and statistical machine translation (smt). under the stssg platform, we compare the expressive abilities of various grammars through synchronous parsing and a real translation platform on a variety of chinese-english bilingual corpora. experimental results show that the stssg is able to better explain the data in parallel corpora than other grammars. our study further finds that the complexity of structure divergence is much higher than suggested in literature, which imposes a big challenge to syntactic transformation-based smt.
pedagogically useful extractive summaries for science education. this paper describes the design and evaluation of an extractive summarizer for educational science content called cogent. cogent extends mead based on strategies elicited from an empirical study with science domain and instructional design experts. cogent identifies sentences containing pedagogically relevant concepts for a specific science domain. the algorithms pursue a hybrid approach integrating both domain independent bottom-up sentence scoring features and domain-aware top-down features. evaluation results indicate that cogent outperforms existing summarizers and generates summaries that closely resemble those generated by human experts. cogent concept inventories appear to also support the computational identification of student misconceptions about earthquakes and plate tectonics.
comparative parser performance analysis across grammar frameworks through automatic tree conversion using synchronous grammars. this paper presents a methodology for the comparative performance analysis of the parsers developed for different grammar frameworks. for such a comparison, we need a common representation format of the parsing results since the representation of the parsing results depends on the grammar frameworks; hence they are not directly comparable to each other. we first convert the parsing result to a shallow cfg analysis by using an automatic tree converter based on synchronous grammars. the use of such a shallow representation as a common format has the advantage of reduced noise introduced by the conversion in comparison with the noise produced by the conversion to deeper representations. we compared an hpsg parser with several cfg parsers in our experiment and found that meaningful differences among the parsers' performance can still be observed by such a shallow representation.
an algorithm for adverbial aspect shift. the paper offers a new type of approach to the semantic phenomenon of adverbial aspect shift within the framework of finite-state temporal semantics. the heart of the proposal is a supervaluational concept of underspecification, and the idea of treating the meanings of temporal prepositions as dynamic presuppositions. the simple shifting algorithm used in the present approach derives the correct set of possible readings on the basis of lexical semantic input only, and, furthermore, may claim cognitive plausibility.
sentence type based reordering model for statistical machine translation. many reordering approaches have been proposed for the statistical machine translation (smt) system. however, the information about the type of source sentence is ignored in the previous works. in this paper, we propose a group of novel reordering models based on the source sentence type for chinese-to-english translation. in our approach, an svm-based classifier is employed to classify the given chinese sentences into three types: special interrogative sentences, other interrogative sentences, and non-question sentences. the different reordering models are developed oriented to the different sentence types. our experiments show that the novel reordering models have obtained an improvement of more than 2.65% in bleu for a phrase-based spoken language translation system.
a framework for identifying textual redundancy. the task of identifying redundant information in documents that are generated from multiple sources provides a significant challenge for summarization and qa systems. traditional clustering techniques detect redundancy at the sentential level and do not guarantee the preservation of all information within the document. we discuss an algorithm that generates a novel graph-based representation for a document and then utilizes a set cover approximation algorithm to remove redundant text from it. our experiments show that this approach offers a significant performance advantage over clustering when evaluated over an annotated dataset.
acquiring sense tagged examples using relevance feedback. supervised approaches to word sense disambiguation (wsd) have been shown to outperform other approaches but are hampered by reliance on labeled training examples (the data acquisition bottleneck). this paper presents a novel approach to the automatic acquisition of labeled examples for wsd which makes use of the information retrieval technique of relevance feedback. this semi-supervised method generates additional labeled examples based on existing annotated data. our approach is applied to a set of ambiguous terms from biomedical journal articles and found to significantly improve the performance of a state-of-the-art wsd system.
weakly supervised supertagging with grammar-informed initialization. much previous work has investigated weak supervision with hmms and tag dictionaries for part-of-speech tagging, but there have been no similar investigations for the harder problem of supertagging. here, i show that weak supervision for supertagging does work, but that it is subject to severe performance degradation when the tag dictionary is highly ambiguous. i show that lexical category complexity and information about how supertags may combine syntactically can be used to initialize the transition distributions of a first-order hidden markov model for weakly supervised learning. this initialization proves more effective than starting with uniform transitions, especially when the tag dictionary is highly ambiguous.
a fluid knowledge representation for understanding and generating creative metaphors. creative metaphor is a phenomenon that stretches and bends the conventions of semantic description, often to humorous and poetic extremes. the computational modeling of metaphor thus requires a knowledge representation that is just as stretchable and semantically accommodating. we present here a flexible knowledge representation for metaphor interpretation and generation, called talking points, and describe how talking points can be acquired on a large scale from wordnet (fellbaum, 1998) and from the web. we show how talking points can be fluidly connected to form a slipnet, and demonstrate that talking points provide an especially concise representation for concepts in general.
estimation of conditional probabilities with decision trees and an application to fine-grained pos tagging. we present a hmm part-of-speech tagging method which is particularly suited for pos tagsets with a large number of fine-grained tags. it is based on three ideas: (1) splitting of the pos tags into attribute vectors and decomposition of the contextual pos probabilities of the hmm into a product of attribute probabilities, (2) estimation of the contextual probabilities with decision trees, and (3) use of high-order hmms. in experiments on german and czech data, our tagger outperformed state-of-the-art pos taggers.
understanding and summarizing answers in community-based question answering services. community-based question answering (cqa) services have accumulated millions of questions and their answers over time. in the process of accumulation, cqa services assume that questions always have unique best answers. however, with an in-depth analysis of questions and answers on cqa services, we find that the assumption cannot be true. according to the analysis, at least 78% of the cqa best answers are reusable when similar questions are asked again, but no more than 48% of them are indeed the unique best answers. we conduct the analysis by proposing taxonomies for cqa questions and answers. to better reuse the cqa content, we also propose applying automatic summarization techniques to summarize answers. our results show that question-type oriented summarization techniques can improve cqa answer quality significantly.
modeling chinese documents with topical word-character models. as chinese text is written without word boundaries, effectively recognizing chinese words is like recognizing collocations in english, substituting characters for words and words for collocations. however, existing topical models that involve collocations have a common limitation. instead of directly assigning a topic to a collocation, they take the topic of a word within the collocation as the topic of the whole collocation. this is unsatisfactory for topical modeling of chinese documents. thus, we propose a topical word-character model (twc), which allows two distinct types of topics: word topic and character topic. we evaluated twc both qualitatively and quantitatively to show that it is a powerful and a promising topic model.
ontonotes: corpus cleanup of mistaken agreement using word sense disambiguation. annotated corpora are only useful if their annotations are consistent. most large-scale annotation efforts take special measures to reconcile inter-annotator disagreement. to date, however, no-one has investigated how to automatically determine exemplars in which the annotators agree but are wrong. in this paper, we use ontonotes, a large-scale corpus of semantic annotations, including word senses, predicate-argument structure, ontology linking, and coreference. to determine the mistaken agreements in word sense annotation, we employ word sense disambiguation (wsd) to select a set of suspicious candidates for human evaluation. experiments are conducted from three aspects (precision, cost-effectiveness ratio, and entropy) to examine the performance of wsd. the experimental results show that wsd is most effective on identifying erroneous annotations for highly-ambiguous words, while a baseline is better for other cases. the two methods can be combined to improve the cleanup process. this procedure allows us to find approximately 2% remaining erroneous agreements in the ontonotes corpus. a similar procedure can be easily defined to check other annotated corpora.
integrating a unification-based semantics in a large scale lexicalised tree adjoining grammar for french. in contrast to lfg and hpsg, there is to date no large scale tree adjoining grammar (tag) equiped with a compositional semantics. in this paper, we report on the integration of a unification-based semantics into a feature-based lexicalised tag for french consisting of around 6 000 trees. we focus on verb semantics and show how factorisation can be used to support a compact and principled encoding of the semantic information that needs to be associated with each of the verbal elementary trees. the factorisation is made possible by the use of xmg, a high-level linguistic formalism designed to specify and compile computational grammars and in particular, grammars based on non-local trees or tree descriptions.
generation of referring expressions: managing structural ambiguities. existing algorithms for the generation of referring expressions tend to generate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. this paper considers how these algorithms should deal with surface ambiguity, focussing on structural ambiguity. we propose that not all ambiguity is worth avoiding, and suggest some ways forward that attempt to avoid unwanted interpretations. we sketch the design of an algorithm motivated by our experimental findings.
a joint information model for n-best ranking. in this paper, we present a method for modeling joint information when generating n-best lists. we apply the method to a novel task of characterizing the similarity of a group of terms where only a small set of many possible semantic properties may be displayed to a user. we demonstrate that considering the results jointly, by accounting for the information overlap between results, generates better n-best lists than considering them independently. we propose an information theoretic objective function for modeling the joint information in an n-best list and show empirical evidence that humans prefer the result sets produced by our joint model. our results show with 95% confidence that the n-best lists generated by our joint ranking model are significantly different from a baseline independent model 50.0% &plusmn; 3.1% of the time, out of which they are preferred 76.6% &plusmn; 5.2% of the time.
efficiently parsing with the product-free lambek calculus. this paper provides a parsing algorithm for the lambek calculus which is polynomial time for a more general fragment of the lambek calculus than any previously known algorithm. the algorithm runs in worst-case time o(n5) when restricted to a certain fragment of the lambek calculus which is motivated by empirical analysis. in addition, a set of parameterized inputs are given, showing why the algorithm has exponential worst-case running time for the lambek calculus in general.