waashk/dblp huggingface dataset:
factorizing complex models: a case study in mention detection. as natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristics, more linguistic properties, deeper semantic and syntactic features. one such example, explored in this article, is the mention detection and recognition task in the automatic content extraction project, with the goal of identifying named, nominal or pronominal references to real-world entities---mentions---and labeling them with three types of information: entity type, entity subtype and mention type. in this article, we investigate three methods of assigning these related tags and compare them on several data sets. a system based on the methods presented in this article participated and ranked very competitively in the ace'04 evaluation.
dynamic nonlocal language modeling via hierarchical topic-based adaptation. this paper presents a novel method of generating and applying hierarchical, dynamic topic-based language models. it proposes and evaluates new cluster generation, hierarchical smoothing and adaptive topic-probability estimation techniques. these combined models help capture long-distance lexical dependencies. experiments on the broadcast news corpus show significant improvement in perplexity (10.5% overall and 33.5% on target vocabulary).
free indexation: combinatorial analysis and a compositional algorithm. the principle known as 'free indexation' plays an important role in the determination of the referential properties of noun phrases in the principle-and-parameters language framework. first, by investigating the combinatorics of free indexation, we show that the problem of enumerating all possible indexings requires exponential time. secondly, we exhibit a provably optimal free indexation algorithm.
on reversing the generation process in optimality theory. optimality theory, a constraint-based phonology and morphology paradigm, has allowed linguists to make elegant analyses of many phenomena, including infixation and reduplication. in this work-in-progress, we build on the work of ellison (1994) to investigate the possibility of using ot as a parsing tool that derives underlying forms from surface forms.
a maximum entropy/minimum divergence translation model. i present empirical comparisons between a linear combination of standard statistical language and translation models and an equivalent maximum entropy/minimum divergence (memd) model, using several different methods for automatic feature selection. the memd model significantly outperforms the standard model in test corpus perplexity, even though it has far fewer parameters.
multimodal generation in the comic dialogue system. we describe how context-sensitive, user-tailored output is specified and produced in the comic multimodal dialogue system. at the conference, we will demonstrate the user-adapted features of the dialogue manager and text planner.
guiding a constraint dependency parser with supertags. we investigate the utility of supertag information for guiding an existing dependency parser of german. using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. the paper reports on a series of experiments using varying models of supertags that significantly increase the parsing accuracy. in addition, an upper bound on the accuracy that can be achieved with perfect supertags is estimated.
hybrid parsing: using probabilistic models as predictors for a symbolic parser. in this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. by including a chunker, a supertagger, a pp attacher, and a fast probabilistic parser we were able to improve upon the baseline by 3.2%, bringing the overall labelled accuracy to 91.1% on the german negra corpus. we attribute the successful integration to the ability of the underlying grammar model to combine uncertain evidence in a soft manner, thus avoiding the problem of error propagation.
the benefit of stochastic pp attachment to a rule-based parser. to study pp attachment disambiguation as a benchmark for empirical methods in natural language processing it has often been reduced to a binary decision problem (between verb or noun attachment) in a particular syntactic configuration. a parser, however, must solve the more general task of deciding between more than two alternatives in many different contexts. we combine the attachment predictions made by a simple model of lexical attraction with a full-fledged parser of german to determine the actual benefit of the subtask to parsing. we show that the combination of data-driven and rule-based components can reduce the number of all parsing errors by 14% and raise the attachment accuracy for dependency parsing of german to an unprecedented 92%.
from route descriptions to sketches: a model for a text-to-image translator. this paper deals with the automatic translation of route descriptions into graphic sketches. we discuss some general problems implied by such inter-mode transcription. we propose a model for an automatic text-to-image translator with a two-stage intermediate representation in which the linguistic representation of a route description precedes the creation of its conceptual representation.
learning more effective dialogue strategies using limited dialogue move features. we explore the use of restricted dialogue contexts in reinforcement learning (rl) of effective dialogue strategies for information seeking spoken dialogue systems (e.g. communicator (walker et al., 2001)). the contexts we use are richer than previous research in this area, e.g. (levin and pieraccini, 1997; scheffler and young, 2001; singh et al., 2002; pietquin, 2004), which use only slot-based information, but are much less complex than the full dialogue "information states" explored in (henderson et al., 2005), for which tractabe learning is an issue. we explore how incrementally adding richer features allows learning of more effective dialogue strategies. we use 2 user simulations learned from communicator data (walker et al., 2001; georgila et al., 2005b) to explore the effects of different features on learned dialogue strategies. our results show that adding the dialogue moves of the last system and user turns increases the average reward of the automatically learned strategies by 65.9% over the original (hand-coded) communicator systems, and by 7.8% over a baseline rl policy that uses only slot-status features. we show that the learned strategies exhibit an emergent "focus switching" strategy and effective use of the 'give help' action.
licensing and tree adjoining grammar in government binding parsing. this paper presents an implemented, psychologically plausible parsing model for government binding theory grammars. i make use of two main ideas: (1) a generalization of the licensing relations of [abney, 1986] allows for the direct encoding of certain principles of grammar (e.g. theta criterion, case filter) which drive structure building; (2) the working space of the parser is constrained to the domain determined by a tree adjoining grammar elementary tree. all dependencies and constraints are localized within this bounded structure. the resultant parser operates in linear time and allows for incremental semantic interpretation and determination of grammaticality.
integrated shallow and deep parsing: topp meets hpsg. we present a novel, data-driven method for integrated shallow and deep parsing. mediated by an xml-based multi-layer annotation architecture, we interleave a robust, but accurate stochastic topological field parser of german with a constraint-based hpsg parser. our annotation-based method for dovetailing shallow and deep phrasal constraints is highly flexible, allowing targeted and fine-grained guidance of constraint-based parsing. we conduct systematic experiments that demonstrate substantial performance gains.
incorporating context information for the extraction of terms. the information used for the extraction of terms can be considered as rather 'internal', i.e. coming from the candidate string itself. this paper presents the incorporation of 'external' information derived from the context of the candidate string. it is embedded to the c-value approach for automatic term recognition (atr), in the form of weights constructed from statistical characteristics of the context words of the candidate string.
independence assumptions considered harmful. many current approaches to statistical language modeling rely on independence assumptions between the different explanatory variables. this results in models which are computationally simple, but which only model the main effects of the explanatory variables on the response variable. this paper presents an argument in favor of a statistical approach that also models the interactions between the explanatory variables. the argument rests on empirical evidence from two series of experimetns concerning automatic ambiguity resolution.
semi-supervised training for statistical word alignment. we introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional expectation maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small, manually word-aligned sub-corpus. we show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality.
toward general-purpose learning for information extraction. two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broadening of the central problem definition to include many non-traditional text domains. this development calls for information extraction systems which are as retargetable and general as possible. here, we describe srv, a learning architecture for information extraction which is designed for maximum generality and flexibility. srv can exploit domain-specific information, including linguistic syntax and lexical information, in the form of features provided to the system explicitly as input for training. this process is illustrated using a domain created from reuters corporate acquisitions articles. features are derived from two general-purpose nlp systems, sleator and temperly's link grammar parser and wordnet. experiments compare the learner's performance with and without such linguistic information. surprisingly, in many cases, the system performs as well without this information as with it.
a general computational treatment of the comparative. we present a general treatment of the comparative that is based on more basic linguistic elements so that the underlying system can be effectively utilized: in the syntactic analysis phase, the comparative is treated the same as similar structures; in the syntactic regularization phase, the comparative is transformed into a standard form so that subsequent processing is basically unaffected by it. the scope of quantifiers under the comparative is also integrated into the system in a general way.
semi-supervised learning of partial cognates using bilingual bootstrapping. partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. detecting the actual meaning of a partial cognate in context can be useful for machine translation tools and for computer-assisted language learning tools. in this paper we propose a supervised and a semi-supervised method to disambiguate partial cognates between two languages: french and english. the methods use only automatically-labeled data; therefore they can be applied for other pairs of languages as well. we also show that our methods perform well when using corpora from different domains.
japanese morphological analyzer using word co-occurence -jtag. we developed a japanese morphological analyzer that uses the co-occurrence of words to select the correct sequence of words in an unsegmented japanese sentence. the co-occurrence information can be obtained from cases where the system incorrectly analyzes sentences. as the amount of information increases, the accuracy of the system increases with a small risk of degradation. experimental results show that the proposed system assigns the correct phonological representations to unsegmented japanese sentences more precisely than do other popular systems.
minimal recursion semantics as dominance constraints: translation, evaluation, and analysis. we show that a practical translation of mrs descriptions into normal dominance constraints is feasible. we start from a recent theoretical translation and verify its assumptions on the outputs of the english resource grammar (erg) on the redwoods corpus. the main assumption of the translation---that all relevant underspecified descriptions are nets---is validated for a large majority of cases; all non-nets computed by the erg seem to be systematically incomplete.
utilizing the world wide web as an encyclopedia: extracting term descriptions from semi-structured texts. in this paper, we propose a method to extract descriptions of technical terms from web pages in order to utilize the world wide web as an encyclopedia. we use linguistic patterns and html text structures to extract text fragments containing term descriptions. we also use a language model to discard extraneous descriptions, and a clustering method to summarize resultant descriptions. we show the effectiveness of our method by way of experiments.
oganizing encyclopedic knowledge based on the web and its application to question answering. we propose a method to generate large-scale encyclopedic knowledge, which is valuable for much nlp research, based on the web. we first search the web for pages containing a term in question. then we use linguistic patterns and html structures to extract text fragments describing the term. finally, we organize extracted term descriptions based on word senses and domains. in addition, we apply an automatically generated encyclopedia to a question answering system targeting the japanese information-technology engineers examination.
an implemented description of japanese: the lexeed dictionary and the hinoki treebank. in this paper we describe the current state of a new japanese lexical resource: the hinoki treebank. the treebank is built from dictionary definition sentences, and uses an hpsg based japanese grammar to encode both syntactic and semantic information. it is combined with an ontology based on the definition sentences to give a detailed sense level description of the most familiar 28,000 words of japanese.
using bilingual comparable corpora and semi-supervised clustering for topic tracking. we address the problem dealing with skewed data, and propose a method for estimating effective training stories for the topic tracking task. for a small number of labelled positive stories, we extract story pairs which consist of positive and its associated stories from bilingual comparable corpora. to overcome the problem of a large number of labelled negative stories, we classify them into some clusters. this is done by using k-means with em. the results on the tdt corpora show the effectiveness of the method.
a hardware algorithm for high speed morpheme extraction and its implementation. this paper describes a new hardware algorithm for morpheme extraction and its implementation on a specific machine (mex-i), as the first step toward achieving natural language parsing accelerators. it also shows the machine's performance, 100--1,000 times faster than a personal computer. this machine can extract morphemes from 10,000 character japanese text by searching an 80,000 morpheme dictionary in 1 second. it can treat multiple text streams, which are composed of character candidates, as well as one text stream. the algorithm is implemented on the machine in linear time for the number of candidates, while conventional sequential algorithms are implemented in combinational time.
a pattern matching method for finding noun and proper noun translations from noisy parallel corpora. we present a pattern matching method for compiling a bilingual lexicon of nouns and proper nouns from unaligned, noisy parallel texts of asian/indo-european language pairs. tagging information of one language is used. word frequency and position information for high and low frequency words are represented in two different vector forms for pattern matching. new anchor point finding and noise elimination techniques are introduced. we obtained a 73.1% precision. we also show how the results can be used in the compilation of domain-specific noun phrases.
robust word sense translation by em learning of frame semantics. we propose a robust method of automatically constructing a bilingual word sense dictionary from readily available monolingual ontologies by using estimation-maximization, without any annotated training data or manual tuning. we demonstrate our method on the english framenet and chinese hownet structures. owing to the robustness of em iterations in improving translation likelihoods, our word sense translation accuracies are very high, at 82% on average, for the 11 most ambiguous words in the english framenet with 5 senses or more. we also carried out a pilot study on using this automatically generated bilingual word sense dictionary to choose the best translation candidates and show the first significant evidence that frame semantics are useful for translation disambiguation. translation disambiguation accuracy using frame semantics is 75%, compared to 15% by using dictionary glossing only. these results demonstrate the great potential for future application of bilingual frame semantics to machine translation tasks.
automatic speech recognition and its application to information extraction. this paper describes recent progress and the author's perspectives of speech recognition technology. applications of speech recognition technology can be classified into two main areas, dictation and human-computer dialogue systems. in the dictation domain, the automatic broadcast news transcription is now actively investigated, especially under the darpa project. the broadcast news dictation technology has recently been integrated with information extraction and retrieval technology and many application systems, such as automatic voice document indexing and retrieval systems, are under development. in the human-computer interaction domain, a variety of experimental systems for information retrieval through spoken dialogue are being investigated. in spite of the remarkable recent progress, we are still behind our ultimate goal of understanding free conversational speech uttered by any speaker under any environment. this paper also describes the most important research issues that we should attack in order to advance to our ultimate goal of fluent speech recognition.
splitting long or ill-formed input for robust spoken-language translation. this paper proposes an input-splitting method for translating spoken-language which includes many long or ill-formed expressions. the proposed method splits input into well-balanced translation units based on a semantic distance calculation. the splitting is performed during left-to-right parsing, and does not degrade translation efficiency. the complete translation result is formed by concatenating the partial translation results of each split unit. the proposed method can be incorporated into frameworks like tdmt, which utilize left-to-right parsing and a score for a substructure. experimental results show that the proposed method gives tdmt the following advantages: (1) elimination of null outputs, (2) splitting of utterances into sentences, and (3) robust translation of erroneous speech recognition results.
combining acoustic and pragmatic features to predict recognition performance in spoken dialogue systems. we use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-best recognition hypotheses to a spoken dialogue system. our best results show a 25% weighted f-score improvement over a baseline system that implements a "grammar-switching" approach to context-sensitive speech recognition.
automatic extraction of subcorpora based on subcategorization frames from a part-of-speech tagged corpus. this paper presents a method for extracting subcorpora documenting different subcategorization frames for verbs, nouns, and adjectives in the 100 mio. word british national corpus. the extraction tool consists of a set of batch files for use with the corpus query processor (cqp), which is part of the ims corpus workbench (cf. christ 1994a, b). a macroprocessor has been developed that allows the user to specify in a simple input file which subcorpora are to be created for a given lemma.the resulting subcorpora can be used (1) to provide evidence for the subcategorization properties of a given lemma, and to facilitate the selection of corpus lines for lexicographic research, and (2) to determine the frequencies of different syntactic contexts of each lemma.
a program for aligning sentences in bilingual corpora. researchers in both machine translation (e.g., brown et al. 1990) and bilingual lexicography (e.g., klavans and tzoukermann 1990) have recently become interested in studying bilingual corpora, bodies of text such as the canadian hansards (parliamentary proceedings), which are available in multiple languages (such as french and english). one useful step is to align the sentences, that is, to identify correspondences between sentences in one language and sentences in the other language.this paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. the program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend to be translated into shorter sentences. a probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. this probabilistic score is used in a dynamic programming framework to find the maximum likelihood alignment of sentences.it is remarkable that such a simple approach works as well as it does. an evaluation was performed based on a trilingual corpus of economic reports issued by the union bank of switzerland (ubs) in english, french, and german. the method correctly aligned all but 4% of the sentences. moreover, it is possible to extract a large subcorpus that has a much smaller error rate. by selecting the best-scoring 80% of the alignments, the error rate is reduced from 4% to 0.7%. there were more errors on the english-french subcorpus than on the english-german subcorpus, showing that error rates will depend on the corpus considered; however, both were small enough to hope that the method will be useful for many language pairs.to further research on bilingual corpora, a much larger sample of canadian hansards (approximately 90 million words, half in english and and half in french) has been aligned with the align program and will be available through the data collection initiative of the association for computational linguistics (acl/dci). in addition, in order to facilitate replication of the align program, an appendix is provided with detailed c-code of the more difficult core of the align program.