waashk/dblp huggingface dataset:
stochastic iterative alignment for machine translation evaluation. a number of metrics for automatic evaluation of machine translation have been proposed in recent years, with some metrics focusing on measuring the adequacy of mt output, and other metrics focusing on fluency. adequacy-oriented metrics such as bleu measure &eta;-gram overlap of mt outputs and their references, but do not represent sentence-level information. in contrast, fluency-oriented metrics such as rouge-w compute longest common subsequences, but ignore words not aligned by the lcs. we propose a metric based on stochastic iterative string alignment (sia), which aims to combine the strengths of both approaches. we compare sia with existing metrics, and find that it outperforms them in overall evaluation, and works specially well in fluency evaluation.
an intermediate representation for the interpretation of temporal expressions. the interpretation of temporal expressions in text is an important constituent task for many practical natural language processing tasks, including question-answering, information extraction and text summarisation. although temporal expressions have long been studied in the research literature, it is only more recently, with the impetus provided by exercises like the ace program, that attention has been directed to broad-coverage, implemented systems. in this paper, we describe our approach to intermediate semantic representations in the interpretation of temporal expressions.
dynamically generating a protein entity dictionary using online resources. with the overwhelming amount of biological knowledge stored in free text, natural language processing (nlp) has received much attention recently to make the task of managing information recorded in free text more feasible. one requirement for most nlp systems is the ability to accurately recognize biological entity terms in free text and the ability to map these terms to corresponding records in databases. such task is called biological named entity tagging. in this paper, we present a system that automatically constructs a protein entity dictionary, which contains gene or protein names associated with uniprot identifiers using online resources. the system can run periodically to always keep up-to-date with these online resources. using online resources that were available on dec. 25, 2004, we obtained 4,046,733 terms for 1,640,082 entities. the dictionary can be accessed from the following website: http://biocreative.ifsm.umbc.edu/biothesaurus/.
should we translate the documents or the queries in cross-language information retrieval? previous comparisons of document and query translation suffered difficulty due to differing quality of machine translation in these two opposite directions. we avoid this difficulty by training identical statistical translation models for both translation directions using the same training data. we investigate information retrieval between english and french, incorporating both translations directions into both document translation and query translation-based information retrieval, as well as into hybrid systems. we find that hybrids of document and query translation-based systems out-perform query translation systems, even human-quality query translation systems.
log-linear models for word alignment. we present a framework for word alignment based on log-linear models. all knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible additional variables. log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. in this paper, we use ibm model 3 alignment probabilities, pos correspondence, and bilingual dictionary coverage as features. our experiments show that log-linear models significantly outperform ibm translation models.
detecting verbal participation in diathesis alternations. we present a method for automatically identifying verbal participation in diathesis alternations. automatically acquired subcategorization frames are compared to a hand-crafted classification for selecting candidate verbs. the minimum description length principle is then used to produce a model and cost for storing the head noun instances from a training corpus at the relevant argument slots. alternating subcategorization frames are identified where the data from corresponding argument slots in the respective frames can be combined to produce a cheaper model than that produced if the data is encoded separately.
tree-to-string alignment template for statistical machine translation. we present a novel translation model based on tree-to-string alignment template (tat) which describes the alignment between a source parse tree and a target string. a tat is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. the model is linguistically syntax-based because tats are extracted automatically from word-aligned, source side parsed parallel texts. to translate a source sentence, we first employ a parser to produce a source parse tree and then apply tats to transform the tree into a target string. our experiments show that the tat-based model significantly outperforms pharaoh, a state-of-the-art decoder for phrase-based models.
finding predominant word senses in untagged text. in word sense disambiguation (wsd), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. the problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. we present work on the use of a thesaurus acquired from raw textual corpora and the wordnet similarity package to find predominant noun senses automatically. the acquired predominant senses give a precision of 64% on the nouns of the senseval-2 english all-words task. this is a very promising result given that our method does not require any hand-tagged text, such as semcor. furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora.
an empirical study on thematic knowledge acquisition based on syntactic clues and heuristics. thematic knowledge is a basis of semantic interpretation. in this paper, we propose an acquisition method to acquire thematic knowledge by exploiting syntactic clues from training sentences. the syntactic clues, which may be easily collected by most existing syntactic processors, reduce the hypothesis space of the thematic roles. the ambiguities may be further resolved by the evidences either from a trainer or from a large corpus. a set of heuristics based on linguistic constraints is employed to guide the ambiguity resolution process. when a trainer is available, the system generates new sentences whose thematic validities can be justified by the trainer. when a large corpus is available, the thematic validity may be justified by observing the sentences in the corpus. using this way, a syntactic processor may become a thematic recognizer by simply deriving its thematic knowledge from its own syntactic knowledge.
reranking and self-training for parser adaptation. statistical parsers trained and tested on the penn wall street journal (wsj) treebank have shown vast improvements over the last 10 years. much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the wsj treebank data. this has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. such worries have merit. the standard "charniak parser" checks in at a labeled precision-recall f-measure of 89.7% on the penn wsj test set, but only 82.9% on the test set from the brown treebank corpus.this paper should allay these fears. in particular, we show that the reranking parser described in charniak and johnson (2005) improves performance of the parser on brown to 85.2%. furthermore, use of the self-training techniques described in (mcclosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled brown data. this is remarkable since training the parser and reranker on labeled brown data achieves only 88.4%.
using conditional random fields for sentence boundary detection in speech. sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. in previous work, we have developed hidden markov model (hmm) and maximum entropy (maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. in this paper, we evaluate the use of a conditional random field (crf) for this task and relate results with this model to our prior work. we evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. in general, our crf model yields a lower error rate than the hmm and maxent models on the nist sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. this probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources.
modular logic grammars. this report describes a logic grammar formalism, modular logic grammars, exhibiting a high degree of modularity between syntax and semantics. there is a syntax rule compiler (compiling into prolog) which takes care of the building of analysis structures and the interface to a clearly separated semantic interpretation component dealing with scoping and the construction of logical forms. the whole system can work in either a one-pass mode or a two-pass mode. in the one-pass mode, logical forms are built directly during parsing through interleaved calls to semantics, added automatically by the rule compiler. in the two-pass mode, syntactic analysis trees are built automatically in the first pass, and then given to the (one-pass) semantic component. the grammar formalism includes two devices which cause the automatically built syntactic structures to differ from derivation trees in two ways: (1) there is a shift operator, for dealing with left-embedding constructions such as english possessive noun phrases while using right-recursive rules (which are appropriate for prolog parsing). (2) there is a distinction in the syntactic formalism between strong non-terminals and weak non-terminals, which is important for distinguishing major levels of grammar.
pens: a machine-aided english writing system for chinese users. writing english is a big barrier for most chinese users. to build a computer-aided system that helps chinese users not only on spelling checking and grammar checking but also on writing in the way of native-english is a challenging task. although machine translation is widely used for this purpose, how to find an efficient way in which human collaborates with computers remains an open issue. in this paper, based on the comprehensive study of chinese users requirements, we propose an approach to machine aided english writing system, which consists of two components: 1) a statistical approach to word spelling help, and 2) an information retrieval based approach to intelligent recommendation by providing suggestive example sentences. both components work together in a unified way, and highly improve the productivity of english writing. we also developed a pilot system, namely pens (perfect english system). preliminary experiments show very promising results.
a functional approach to generation with tag. it has been hypothesized that tree adjoining grammar (tag) is particularly well suited for sentence generation. it is unclear, however, how a sentence generation system based on tag should choose among the syntactic possibilities made available in the grammar. in this paper we consider the question of what needs to be done to generate with tags and explain a generation system that provides the necessary features. this approach is compared with other tag-based generation systems. particular attention is given to mumble-86 which, like our system, makes syntactic choice on sophisticated functional grounds.
an algorithm for plan recognition in collaborative discourse. a model of plan recognition in discourse must be based on intended recognition, distinguish each agent's beliefs and intentions from the other's, and avoid assumptions about the correctness or completeness of the agents' beliefs. in this paper, we present an algorithm for plan recognition that is based on the shared-plan model of collaboration (grosz and sidner, 1990; lochbaum et al., 1990) and that satisfies these constraints.
discriminating image senses by clustering with multimodal features. we discuss image sense discrimination (isd), and apply a method based on spectral clustering, using multimodal features from the image and text of the embedding web page. we evaluate our method on a new data set of annotated web images, retrieved with ambiguous query terms. experiments investigate different levels of sense granularity, as well as the impact of text and image features, and global versus local text features.
target word selection as proximity in semantic space. lexical selection is a significant problem for wide-coverage machine translation: depending on the context, a given source language word can often be translated into different target language words. in this paper i propose a method for target word selection that assumes the appropriate translation is more similar to the translated context than are the alternatives. similarity of a word to a context is estimated using a proximity measure in corpus-derived "semantic space". the method is evaluated using an english-spanish parallel corpus of colloquial dialogue.
memory-efficient and thread-safe quasi-destructive graph unification. in terms of both speed and memory consumption, graph unification remains the most expensive component of unification-based grammar parsing. we present a technique to reduce the memory usage of unification algorithms considerably, without increasing execution times. also, the proposed algorithm is thread-safe, providing an efficient algorithm for parallel processing as well.
a distributional model of semantic context effects in lexical processing. one of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. we present a computational model of contextual facilitation based on word co-occurrence vectors, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. the aim of our study is to find out whether special-purpose mechanisms are necessary in order to capture the pattern of the experimental results.
a generic approach to parallel chart parsing with an application to lingo. multi-processor systems are becoming more commonplace and affordable. based on analyses of actual parsings, we argue that to exploit the capabilities of such machines, unification-based grammar parsers should distribute work at the level of individual unification operations. we present a generic approach to parallel chart parsing that meets this requirement, and show that an implementation of this technique for lingo achieves considerable speedups.
online large-margin training of dependency parsers. we present an effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training (crammer and singer, 2003; crammer et al., 2003) on top of efficient parsing techniques for dependency trees (eisner, 1996). the trained parsers achieve a competitive dependency accuracy for both english and czech with no language specific enhancements.
incremental dependency parsing. the paper introduces a dependency-based grammar and the associated parser and focusses on the problem of determinism in parsing and recovery from errors. first, it is shown how dependency-based parsing can be afforded, by taking into account the suggestions coming from other approaches, and the preference criteria for parsing are breifly addressed. second, the issues of the interconnection between the syntactic analysis and the semantic interpretation in incremental processing are discussed and the adoption of a tms for the recovery of the processing errors is suggested.
tags as a grammatical formalism for generation. tree adjoining grammars, or "tag's", (joshi, levy & takahashi 1975; joshi 1983; kroch & joshi 1985) were developed as an alternative to the standard syntactic formalisms that are used in theoretical analyses of language. they are attractive because they may provide just the aspects of context sensitive expressive power that actually appear in human languages while otherwise remaining context free.this paper describes how we have applied the theory of tree adjoining grammars to natural language generation. we have been attracted to tag's because their central operation---the extension of an "initial" phrase structure tree through the inclusion, at very specifically constrained locations, of one or more "auxiliary" trees---corresponds directly to certain central operations of our own, performance-oriented theory.we begin by briefly describing tag's as a formalism for phrase structure in a competence theory, and summarize the points in the theory of tag's that are germaine to our own theory. we then consider generally the position of a grammar within the generation process, introducing our use of tag's through a contrast with how others have used systemic grammars. this takes us to the core results of our paper: using examples from our research with weil-written texts from newspapers, we walk through our tag inspired treatments of raising and wh-movement, and show the correspondence of the tag "adjunction" operation and our "attachment" process.in the final section we discuss extensions to the theory, motivated by the way we use the operation corresponding to tag's adjunction in performance. this suggests that the competence theory of tag's can be profitably projected to structures at the morphological level as well as the present syntactic level.
formal aspects and parsing issues of dependency theory. the paper investigates the problem of providing a formal device for the dependency approach to syntax, and to link it with a parsing model. after reviewing the basic tenets of the paradigm and the few existing mathematical results, we describe a dependency formalism which is able to deal with long-distance dependencies. finally, we present an earley-style parser for the formalism and discuss the (polynomial) complexity results.
babel: a testbed for research in origins of language. we believe that language is a complex adaptive system that emerges from adaptive interactions between language users and continues to evolve and adapt through repeated interactions. our research looks at the mechanisms and processes involved in such emergence and adaptation. to provide a basis for our computer simulations, we have implemented an open-ended, extensible testbed called babel which allows rapid construction of experiments and flexible visualization of results.
re-usable tools for precision machine translation. the logon mt demonstrator assembles independently valuable general-purpose nlp components into a machine translation pipeline that capitalizes on output quality. the demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes.
simple algorithms for complex relation extraction with applications to biomedical ie. a complex relation is any n-ary relation in which some of the arguments may be be unspecified. we present here a simple two-stage method for extracting complex relations between named entities in text. the first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. we evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text.
on2l - a framework for incremental ontology learning in spoken dialog systems. an open-domain spoken dialog system has to deal with the challenge of lacking lexical as well as conceptual knowledge. as the real world is constantly changing, it is not possible to store all necessary knowledge beforehand. therefore, this knowledge has to be acquired during the run time of the system, with the help of the out-of-vocabulary information of a speech recognizer. as every word can have various meanings depending on the context in which it is uttered, additional context information is taken into account, when searching for the meaning of such a word. in this paper, i will present the incremental ontology learning framework on2l. the defined tasks for the framework are: the hypernym extraction from internet texts for unknown terms delivered by the speech recognizer; the mapping of those and their hypernyms into ontological concepts and instances; and the following integration of them into the system's ontology.
confirmation in multimodal systems. systems that attempt to understand natural human input make mistakes, even humans. however, humans avoid misunderstandings by confirming doubtful input. multimodal systems---those that combine simultaneous input from more than one modality, for example speech and gesture-have historically been designed so that they either request confirmation of speech, their primary modality, or not at all. instead, we experimented with delaying confirmation until after the speech and gesture were combined into a complete multimodal command. in controlled experiments, subjects achieved more commands per minute at a lower error rate when the system delayed confirmation, than compared to when subjects confirmed only speech. in addition, this style of late confirmation meets the user's expectation that confirmed commands should be executable.
alternative approaches for generating bodies of grammar rules. we compare two approaches for describing and generating bodies of rules used for natural language parsing. in today's parsers rule bodies do not exist a priori but are generated on the fly, usually with methods based on n-grams, which are one particular way of inducing probabilistic regular languages. we compare two approaches for inducing such languages. one is based on n-grams, the other on minimization of the kullback-leibler divergence. the inferred regular languages are used for generating bodies of rules inside a parsing procedure. we compare the two approaches along two dimensions: the quality of the probabilistic regular language they produce, and the performance of the parser they were used to build. the second approach outperforms the first one along both dimensions.
probabilistic disambiguation models for wide-coverage hpsg parsing. this paper reports the development of log-linear models for the disambiguation in wide-coverage hpsg parsing. the estimation of log-linear models requires high computational cost, especially with wide-coverage grammars. using techniques to reduce the estimation cost, we trained the models using 20 sections of penn tree-bank. a series of experiments empirically evaluated the estimation techniques, and also examined the performance of the disambiguation models on the parsing of real-world sentences.