waashk/dblp huggingface dataset:
extractive summarization using inter- and intra- event relevance. event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. in this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of internal association, semantic relatedness, distributional similarity and named entity clustering. we then apply pagerank ranking algorithm to estimate the significance of an event for inclusion in a summary from the event relevance derived. experiments on the duc 2001 test data shows that the relevance of the named entities involved in events achieves better result when their relevance is derived from the event terms they associate. it also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like word-net.
compacting the penn treebank grammar. treebanks, such as the penn treebank (ptb), offer a simple approach to obtaining a broad coverage grammar: one can simply read the grammar off the parse trees in the treebank. while such a grammar is easy to obtain, a square-root rate of growth of the rule set with corpus size suggests that the derived grammar is far from complete and that much more treebanked text would be required to obtain a complete grammar, if one exists at some limit. however, we offer an alternative explanation in terms of the underspecification of structures within the treebank. this hypothesis is explored by applying an algorithm to compact the derived grammar by eliminating redundant rules - rules whose right hand sides can be parsed by other rules. the size of the resulting compacted grammar, which is significantly less than that of the full treebank grammar, is shown to approach a limit. however, such a compacted grammar does not yield very good performance figures. a version of the compaction algorithm taking rule probabilities into account is proposed, which is argued to be more linguistically motivated. combined with simple thresholding, this method can be used to give a 58% reduction in grammar size without significant change in parsing performance, and can produce a 69% reduction with some gain in recall, but a loss in precision.
discriminative pruning of language models for chinese word segmentation. this paper presents a discriminative pruning method of n-gram language model for chinese word segmentation. to reduce the size of the language model that is used in a chinese word segmentation system, importance of each bigram is computed in terms of discriminative pruning criterion that is related to the performance loss caused by pruning the bigram. then we propose a step-by-step growing algorithm to build the language model of desired size. experimental results show that the discriminative pruning method leads to a much smaller model compared with the model pruned using the state-of-the-art method. at the same chinese word segmentation f-measure, the number of bigrams in the model can be reduced by up to 90%. correlation between language model perplexity and word segmentation performance is also discussed.
sense-linking in a machine readable dictionary. dictionaries contain a rich set of relationships between their senses, but often these relationships are only implicit. we report on our experiments to automatically identify links between the senses in a machine-readable dictionary. in particular, we automatically identify instances of zero-affix morphology, and use that information to find specific linkages between senses. this work has provided insight into the performance of a stochastic tagger.
sentence fragments regular structures. this paper describes an analysis of telegraphic fragments as regular structures (not errors) handled by minimal extensions to a system designed for processing the standard language. the modular approach which has been implemented in the unisys natural language processing system pundit is based on a division of labor in which syntax regulates the occurrence and distribution of elided elements, and semantics and pragmatics use the system's standard mechanisms to interpret them.
an end-to-end discriminative approach to machine translation. we present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. unlike discriminative reranking approaches, our system can take advantage of learned features in all stages of decoding. we first discuss several challenges to error-driven discriminative approaches. in particular, we explore different ways of updating parameters given a training example. we find that making frequent but smaller updates is preferable to making fewer but larger updates. then, we discuss an array of features and show both how they quantitatively increase bleu score and how they qualitatively interact on specific examples. one particular feature we investigate is a novel way to introduce learning into the initial phrase extraction process, which has previously been entirely heuristic.
morphological cues for lexical semantics. most natural language processing tasks require lexical semantic information. automated acquisition of this information would thus increase the robustness and portability of nlp systems. this paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information. one advantage of this method, and of other methods that rely only on surface characteristics of language, is that the necessary input is currently available.
are these documents written from different perspectives? a test of different perspectives based on statistical distribution divergence. in this paper we investigate how to automatically determine if two document collections are written from different perspectives. by perspectives we mean a point of view, for example, from the perspective of democrats or republicans. we propose a test of different perspectives based on distribution divergence between the statistical models of two collections. experimental results show that the test can successfully distinguish document collections of different perspectives from other types of collections.
automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. in this paper we describe two new objective automatic evaluation methods for machine translation. the first method is based on longest common subsequence between a candidate translation and a set of reference translations. longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring in-sequence n-grams automatically. the second method relaxes strict n-gram matching to skip-bigram matching. skip-bigram is any pair of words in their sentence order. skip-bigram cooccurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. the empirical results show that both methods correlate with human judgments very well in both adequacy and fluency.
learning constraint grammar-style disambiguation rules using inductive logic programming. this paper reports a pilot study, in which constraint grammar inspired rules were learnt using the progol machine-learning system. rules discarding faulty readings of ambiguously tagged words were learnt for the part of speech tags of the stockholm-ume&aring; corpus. several thousand disambiguation rules were induced. when tested on unseen data, 98% of the words retained the correct reading after tagging. however, there were ambiguities pending after tagging, on an average 1.13 tags per word. the results suggest that the progol system can be useful for learning tagging rules of good quality.
generating precondition expressions in instructional text. this study employs a knowledge intensive corpus analysis to identify the elements of the communicative context which can be used to determine the appropriate lexical and grammatical form of instructional texts. imagene, an instructional text generation system based on this analysis, is presented, particularly with reference to its expression of precondition relations.
algorithms for generation in lambek theorem proving. we discuss algorithms for generation within the lambek theorem proving framework. efficient algorithms for generation in this framework take a semantics-driven strategy. this strategy can be modeled by means of rules in the calculus that are geared to generation, or by means of an algorithm for the theorem prover. the latter possibility enables processing of a bidirectional calculus. therefore lambek theorem proving is a natural candidate for a 'uniform' architecture for natural language parsing and generation.
modeling adjectives in computational relational lexica. in this paper we propose a small set of lexical conceptual relations which allow to encode adjectives in computational relational lexica in a principled and integrated way. our main motivation comes from the fact that adjectives and certain classes of verbs, related in a way or another with adjectives, do not have a satisfactory representation in this kind of lexica. this is due to a great extent to the heterogeneity of their semantic and syntactic properties. we sustain that such properties are mostly derived from the relations holding between adjectives and other pos. accordingly, our proposal is mainly concerned with the specification of appropriate cross-pos relations to encode adjectives in lexica of the type considered here.
examining the content load of part of speech blocks for information retrieval. we investigate the connection between part of speech (pos) distribution and content in language. we define pos blocks to be groups of parts of speech. we hypothesise that there exists a directly proportional relation between the frequency of pos blocks and their content salience. we also hypothesise that the class membership of the parts of speech within such blocks reflects the content load of the blocks, on the basis that open class parts of speech are more content-bearing than closed class parts of speech. we test these hypotheses in the context of information retrieval, by syntactically representing queries, and removing from them content-poor blocks, in line with the aforementioned hypotheses. for our first hypothesis, we induce pos distribution information from a corpus, and approximate the probability of occurrence of pos blocks as per two statistical estimators separately. for our second hypothesis, we use simple heuristics to estimate the content load within pos blocks. we use the text retrieval conference (trec) queries of 1999 and 2000 to retrieve documents from the wt2g and wt10g test collections, with five different retrieval strategies. experimental outcomes confirm that our hypotheses hold in the context of information retrieval.
learning to predict pitch accents and prosodic boundaries in dutch. we train a decision tree inducer (cart) and a memory-based classifier (mbl) on predicting prosodic pitch accents and breaks in dutch text, on the basis of shallow, easy-to-compute features. we train the algorithms on both tasks individually and on the two tasks simultaneously. the parameters of both algorithms and the selection of features are optimized per task with iterative deepening, an efficient wrapper procedure that uses progressive sampling of training data. results show a consistent significant advantage of mbl over cart, and also indicate that task combination can be done at the cost of little generalization score loss. tests on cross-validated data and on held-out data yield f-scores of mbl on accent placement of 84 and 87, respectively, and on breaks of 88 and 91, respectively. accent placement is shown to outperform an informed baseline rule; reliably predicting breaks other than those already indicated by intra-sentential punctuation, however, appears to be more challenging.
truecasing. truecasing is the process of restoring case information to badly-cased or non-cased text. this paper explores truecasing issues and proposes a statistical, language modeling based truecaser which achieves an accuracy of ~98% on news articles. task based evaluation shows a 26% f-measure improvement in named entity recognition when using truecasing. in the context of automatic content extraction, mention detection on automatic speech recognition text is also improved by a factor of 8. truecasing also enhances machine translation output legibility and yields a bleu score improvement of 80.2%. this paper argues for the use of truecasing as a valuable component in text processing applications.
cl research's knowledge management system. cl research began experimenting with massive xml tagging of texts to answer questions in trec 2002. in duc 2003, the experiments were extended into text summarization. based on these experiments, the knowledge management system (kms) was developed to combine these two capabilities and to serve as a unified basis for other types of document exploration. kms has been extended to include web question answering, both general and topic-based summarization, information extraction, and document exploration. the document exploration functionality includes identification of semantically similar concepts and dynamic ontology creation. as development of kms has continued, user modeling has become a key research issue: how will different users want to use the information they identify.
comparing two grammar-based generation algorithms: a case study. in this paper we compare two grammar-based generation algorithms: the semantic-head-driven generation algorithm (shdga), and the essential arguments algorithm (eaa). both algorithms have successfully addressed several outstanding problems in grammar-based generation, including dealing with non-monotonic compositionality of representation, left-recursion, deadlock-prone rules, and nondeterminism. we concentrate here on the comparison of selected properties: generality, efficiency, and determinism. we show that eaa's traversals of the analysis tree for a given language construct, include also the one taken on by shdga. we also demonstrate specific and common situations in which shdga will invariably run into serious inefficiency and nondeterminism, and which eaa will handle in an efficient and deterministic manner. we also point out that only eaa allows to treat the underlying grammar in a truly multi-directional manner.
predicting student emotions in computer-human tutoring dialogues. we examine the utility of speech and lexical features for predicting student emotions in computer-human spoken tutoring dialogues. we first annotate student turns for negative, neutral, positive and mixed emotions. we then extract acoustic-prosodic features from the speech signal, and lexical items from the transcribed or recognized speech. we compare the results of machine learning experiments using these features alone or in combination to predict various categorizations of the annotated student emotions. our best results yield a 19-36% relative improvement in error reduction over a baseline. finally, we compare our results with emotion prediction in human-human tutoring dialogues.
bitext correspondences through rich mark-up. rich mark-up can considerably benefit the process of establishing bitext correspondences, that is, the task of providing correct identification and alignment methods for text segments that are translation equivalences of each other in a parallel corpus. we present a sentence alignment algorithm that, by taking advantage of previously annotated texts, obtains accuracy rates close to 100%. the algorithm evaluates the similarity of the linguistic and extralinguistic mark-up in both sides of a bitext. given that annotations are neutral with respect to typological, grammatical and orthographical differences between languages, rich mark-up becomes an optimal foundation to support bitext correspondences. the main originality of this approach is that it makes maximal use of annotations, which is a very sensible and efficient method for the exploitation of parallel corpora when annotations exist.
predicting user reactions to system error. this paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made a speech recognition error. we describe statistical comparisons of features of these aware sites in a train timetable spoken dialogue corpus, which reveal significant prosodic differences between such turns, compared with turns that 'correct' speech recognition errors as well as with 'normal' turns that are neither aware sites nor corrections. we then present machine learning results in which we show how prosodic features in combination with other automatically available features can predict whether or not a user turn was a normal turn, a correction, and/or an aware site.
structural disambiguation with constraint propagation. we present a new grammatical formalism called constraint dependency grammar (cdg) in which every grammatical rule is given as a constraint on word-to-word modifications. cdg parsing is formalized as a constraint satisfaction problem over a finite domain so that efficient constraint-propagation algorithms can be employed to reduce structural ambiguity without generating individual parse trees. the weak generative capacity and the computational complexity of cdg parsing are also discussed.
combining multiple knowledge sources for discourse segmentation. we predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. we present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. when multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).
probabilistic cfg with latent annotations. this paper defines a generative probabilistic model of parse trees, which we call pcfg-la. this model is an extension of pcfg in which non-terminal symbols are augmented with latent variables. fine-grained cfg rules are automatically induced from a parsed corpus by training a pcfg-la model using an em-algorithm. because exact parsing with a pcfg-la is np-hard, several approximations are described and empirically compared. in experiments using the penn wsj corpus, our automatically trained model gave a performance of 86.6% (f1, sentences &le; 40 words), which is comparable to that of an unlexicalized pcfg parser created using extensive manual feature selection.
evaluating response strategies in a web-based spoken dialogue agent. while the notion of a cooperative response has been the focus of considerable research in natural language dialogue systems, there has been little empirical work demonstrating how such responses lead to more efficient, natural, or successful dialogues. this paper presents an experimental evaluation of two alternative response strategies in toot, a spoken dialogue agent that allows users to access train schedules stored on the web via a telephone conversation. we compare the performance of two versions of toot (literal and cooperative), by having users carry out a set of tasks with each version. by using hypothesis testing methods, we show that a combination of response strategy, application task, and task/strategy interactions account for various types of performance differences. by using the paradise evaluation framework to estimate an overall performance function, we identify interdependencies that exist between speech recognition and response strategy. our results elaborate the conditions under which toot's cooperative rather than literal strategy contributes to greater performance.
are: instance splitting strategies for dependency relation-based information extraction. information extraction (ie) is a fundamental technology for nlp. previous methods for ie were relying on co-occurrence relations, soft patterns and properties of the target (for example, syntactic role), which result in problems of handling paraphrasing and alignment of instances. our system are (anchor and relation) is based on the dependency relation model and tackles these problems by unifying entities according to their dependency relations, which we found to provide more invariant relations between entities in many cases. in order to exploit the complexity and characteristics of relation paths, we further classify the relation paths into the categories of 'easy', 'average' and 'hard', and utilize different extraction strategies based on the characteristics of those categories. our extraction method leads to improvement in performance by 3% and 6% for muc4 and muc6 respectively as compared to the state-of-art ie systems.
automatic detection of poor speech recognition at the dialogue level. the dialogue strategies used by a spoken dialogue system strongly influence performance and user satisfaction. an ideal system would not use a single fixed strategy, but would <i>adapt</i> to the circumstances at hand. to do so, a system must be able to identify dialogue properties that suggest adaptation. this paper focuses on identifying situations where the speech recognizer is performing poorly. we adopt a machine learning approach to learn rules from a dialogue corpus for identifying these situations. our results show a significant improvement over the baseline and illustrate that both lower-level acoustic features and higher-level dialogue features can affect the performance of the learning algorithm.
sructural matching of parallel texts. this paper describes a method for finding structural matching between parallel sentences of two languages, (such as japanese and english). parallel sentences are analyzed based on unification grammars, and structural matching is performed by making use of a similarity measure of word pairs in the two languages. syntactic ambiguities are resolved simultaneously in the matching process. the results serve as a useful source for extracting linguistic and lexical knowledge.
building semantic perceptron net for topic spotting. this paper presents an approach to automatically build a semantic perceptron net (spn) for topic spotting. it uses context at the lower layer to select the exact meaning of key words, and employs a combination of context, co-occurrence statistics and thesaurus to group the distributed but semantically related words within a topic to form basic semantic nodes. the semantic nodes are then used to infer the topic within an input document. experiments on reuters 21578 data set demonstrate that spn is able to capture the semantics of topics, and it performs well on topic spotting task.
discourse cues for broadcast news segmentation. this paper describes the design and application of time-enhanced, finite state models of discourse cues to the automated segmentation of broadcast news, we describe our analysis of a broadcast news corpus, the design of a discourse cue based story segmentor that builds upon information extraction techniques, and finally its computational implementation and evaluation in the broadcast news navigator (bnn) to support video news browsing, retrieval, and summarization.