waashk/dblp huggingface dataset:
real-time decision detection in multi-party dialogue. we describe a process for automatically detecting decision-making sub-dialogues in multi-party, human-human meetings in real-time. our basic approach to decision detection involves distinguishing between different utterance types based on the roles that they play in the formulation of a decision. in this paper, we describe how this approach can be implemented in real-time, and show that the resulting system's performance compares well with other detectors, including an off-line version.
an alternative to head-driven approaches for parsing a (relatively) free word-order language. applying statistical parsers developed for english to languages with freer word-order has turned out to be harder than expected. this paper investigates the adequacy of different statistical parsing models for dealing with a (relatively) free word-order language. we show that the recently proposed relational-realizational (rr) model consistently outperforms state-of-the-art head-driven (hd) models on the hebrew treebank. our analysis reveals a weakness of hd models: their intrinsic focus on configurational information. we conclude that the form-function separation ingrained in rr models makes them better suited for parsing nonconfigurational phenomena.
empirical exploitation of click data for task specific ranking. there have been increasing needs for task specific rankings in web search such as rankings for specific query segments like long queries, time-sensitive queries, navigational queries, etc; or rankings for specific domains/contents like answers, blogs, news, etc. in the spirit of "divide-and-conquer", task specific ranking may have potential advantages over generic ranking since different tasks have task-specific features, data distributions, as well as feature-grade correlations. a critical problem for the task-specific ranking is training data insufficiency, which may be solved by using the data extracted from click log. this paper empirically studies how to appropriately exploit click data to improve rank function learning in task-specific ranking. the main contributions are 1) the exploration on the utilities of two promising approaches for click pair extraction; 2) the analysis of the role played by the noise information which inevitably appears in click data extraction; 3) the appropriate strategy for combining training data and click data; 4) the comparison of click data which are consistent and inconsistent with baseline function.
recognizing implicit discourse relations in the penn discourse treebank. we present an implicit discourse relation classifier in the penn discourse treebank (pdtb). our classifier considers the context of the two arguments, word pair information, as well as the arguments' internal constituent and dependency parses. our results on the pdtb yields a significant 14.1% improvement over the baseline. in our error analysis, we discuss four challenges in recognizing implicit relations in the pdtb.
supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. this work investigates design choices in modeling a discourse scheme for improving opinion polarity classification. for this, two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework. both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. we also present quantitative and qualitative analyses showing how the improvements are achieved.
polylingual topic models. topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. meanwhile, massive collections of interlinked documents in dozens of languages, such as wikipedia, are now widely available, calling for tools that can characterize content in many languages. we introduce a polylingual topic model that discovers topics aligned across multiple languages. we explore the model's characteristics using two large corpora, each with over ten different languages, and demonstrate its usefulness in supporting machine translation and tracking topic trends across languages.
semi-supervised learning for semantic relation classification using stratified sampling strategy. this paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation classification. first, the training data is partitioned into several strata according to relation types/subtypes, then relation instances are randomly sampled from each stratum to form the initial seed set. we also investigate different augmentation strategies in iteratively adding reliable instances to the labeled set, and find that the bootstrapping procedure may stop at a reasonable point to significantly decrease the training time without degrading too much in performance. experiments on the ace rdc 2003 and 2004 corpora show the stratified sampling strategy contributes more than the bootstrapping procedure itself. this suggests that a proper sampling strategy is critical in semi-supervised learning.
improved statistical machine translation using monolingually-derived paraphrases. untranslated words still constitute a major problem for statistical machine translation (smt), and current smt systems are limited by the quantity of parallel training texts. augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called "low density" languages. but pivoting requires additional parallel texts. we address this problem by deriving paraphrases monolingually, using distributional semantic similarity measures, thus providing access to larger training resources, such as comparable and unrelated monolingual corpora. we present what is to our knowledge the first successful integration of a collocational approach to untranslated words with an end-to-end, state of the art smt system demonstrating significant translation improvements in a low-resource setting.
automatically evaluating content selection in summarization without human models. we present a fully automatic method for content selection evaluation in summarization that does not require the creation of human model summaries. our work capitalizes on the assumption that the distribution of words in the input and an informative summary of that input should be similar to each other. results on a large scale evaluation from the text analysis conference show that input-summary comparisons are very effective for the evaluation of content selection. our automatic methods rank participating systems similarly to manual model-based pyramid evaluation and to manual human judgments of responsiveness. the best feature, jensen-shannon divergence, leads to a correlation as high as 0.88 with manual pyramid and 0.73 with responsiveness evaluations.
projecting parameters for multilingual word sense disambiguation. we report in this paper a way of doing word sense disambiguation (wsd) that has its origin in multilingual mt and that is cognizant of the fact that parallel corpora, wordnets and sense annotated corpora are scarce resources. with respect to these resources, languages show different levels of readiness; however a more resource fortunate language can help a less resource fortunate language. our wsd method can be applied to a language even when no sense tagged corpora for that language is available. this is achieved by projecting wordnet and corpus parameters from another language to the language in question. the approach is centered around a novel synset based multilingual dictionary and the empirical observation that within a domain the distribution of senses remains more or less invariant across languages. the effectiveness of our approach is verified by doing parameter projection and then running two different wsd algorithms. the accuracy values of approximately 75% (f1-score) for three languages in two different domains establish the fact that within a domain it is possible to circumvent the problem of scarcity of resources by projecting parameters like sense distributions, corpus-co-occurrences, conceptual distance, etc. from one language to another.
web-scale distributional similarity and entity set expansion. computing the pairwise semantic similarity between all words on the web is a computationally challenging task. parallelization and optimizations are necessary. we propose a highly scalable implementation based on distributional similarity, implemented in the mapreduce framework and deployed over a 200 billion word crawl of the web. the pairwise similarity between 500 million terms is computed in 50 hours using 200 quad-core nodes. we apply the learned similarity matrix to the task of automatic set expansion and present a large empirical study to quantify the effect on expansion performance of corpus size, corpus quality, seed composition and seed size. we make public an experimental testbed for set expansion analysis that includes a large collection of diverse entity sets extracted from wikipedia.
segmenting email message text into zones. in the early days of email, widely-used conventions for indicating quoted reply content and email signatures made it easy to segment email messages into their functional parts. today, the explosion of different email formats and styles, coupled with the ad hoc ways in which people vary the structure and layout of their messages, means that simple techniques for identifying quoted replies that used to yield 95% accuracy now find less than 10% of such content. in this paper, we describe zebra, an svm-based system for segmenting the body text of email messages into nine zone types based on graphic, orthographic and lexical cues. zebra performs this task with an accuracy of 87.01%; when the number of zones is abstracted to two or three zone classes, this increases to 93.60% and 91.53% respectively.
semi-supervised speech act recognition in emails and forums. in this paper, we present a semi-supervised method for automatic speech act recognition in email and forums. the major challenge of this task is due to lack of labeled data in these two genres. our method leverages labeled data in the switchboard-damsl and the meeting recorder dialog act database and applies simple domain adaptation techniques over a large amount of unlabeled email and forum data to address this problem. our method uses automatically extracted features such as phrases and dependency trees, called subtree features, for semi-supervised learning. empirical results demonstrate that our model is effective in email and forum speech act recognition.
a syntactified direct translation model with linear-time decoding. recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. the decoders accompanying these extensions typically exceed quadratic time complexity. this paper extends the direct translation model 2 (dtm2) with syntax while maintaining linear-time decoding. we employ a linear-time parsing algorithm based on an eager, incremental interpretation of combinatory categorial grammar (ccg). as every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single supertag-operator pair for extending the dependency parse incrementally. alongside translation features extracted from the derived parse tree, we explore syntactic features extracted from the incremental derivation process. our empirical experiments show that our model significantly outperforms the state-of-the art dtm2 system.
review sentiment scoring via a parse-and-paraphrase paradigm. this paper presents a parse-and-paraphrase paradigm to assess the degrees of sentiment for product reviews. sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. the extraction of lexical features such as unigram/bigram also complicates the sentiment classification task, as linguistic structure such as implicit long-distance dependency is often disregarded. in this paper, we propose an approach to extracting adverb-adjective-noun phrases based on clause structure obtained by parsing sentences into a hierarchical representation. we also propose a robust general solution for modeling the contribution of adverbials and negation to the score for degree of sentiment. in an application involving extracting aspect-based pros and cons from restaurant reviews, we obtained a 45% relative improvement in recall through the use of parsing methods, while also improving precision.
tree kernel-based svm with structured syntactic knowledge for btg-based phrase reordering. structured syntactic knowledge is important for phrase reordering. this paper proposes using convolution tree kernel over source parse tree to model structured syntactic knowledge for btg-based phrase reordering in the context of statistical machine translation. our study reveals that the structured syntactic features over the source phrases are very effective for btg constraint-based phrase reordering and those features can be well captured by the tree kernel. we further combine the structured features and other commonly-used linear features into a composite kernel. experimental results on the nist mt-2005 chinese-english translation tasks show that our proposed phrase reordering model statistically significantly outperforms the baseline methods.
less is more: significance-based n-gram selection for smaller, better language models. the recent availability of large corpora for training n-gram language models has shown the utility of models of higher order than just trigrams. in this paper, we investigate methods to control the increase in model size resulting from applying standard methods at higher orders. we introduce significance-based n-gram selection, which not only reduces model size, but also improves perplexity for several smoothing methods, including katz backoff and absolute discounting. we also show that, when combined with a new smoothing method and a novel variant of weighted-difference pruning, our selection method performs better in the trade-off between model size and perplexity than the best pruning method we found for modified kneser-ney smoothing.
labeled lda: a supervised topic model for credit attribution in multi-labeled corpora. a significant portion of the world's text is tagged by readers on social bookmarking websites. credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. this paper introduces labeled lda, a topic model that constrains latent dirichlet allocation by defining a one-to-one correspondence between lda's latent topics and user tags. this allows labeled lda to directly learn word-tag correspondences. we demonstrate labeled lda's improved expressiveness over traditional lda with visualizations of a corpus of tagged web pages from del.icio.us. labeled lda outperforms svms by more than 3 to 1 when extracting tag-specific document snippets. as a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets.
hypernym discovery based on distributional similarity and hierarchical structures. this paper presents a new method of developing a large-scale hyponymy relation database by combining wikipedia and other web documents. we attach new words to the hyponymy database extracted from wikipedia by using distributional similarity calculated from documents on the web. for a given target word, our algorithm first finds k similar words from the wikipedia database. then, the hypernyms of these k similar words are assigned scores by considering the distributional similarities and hierarchical distances in the wikipedia database. finally, new hyponymy relations are output according to the scores. in this paper, we tested two distributional similarities. one is based on raw verb-noun dependencies (which we call "rvd"), and the other is based on a large-scale clustering of verb-noun dependencies (called "cvd"). our method achieved an attachment accuracy of 91.0% for the top 10,000 relations, and an attachment accuracy of 74.5% for the top 100,000 relations when using cvd. this was a far better outcome compared to the other baseline approaches. excluding the region that had very high scores, cvd was found to be more effective than rvd. we also confirmed that most relations extracted by our method cannot be extracted merely by applying the well-known lexico-syntactic patterns to web documents.
mining search engine clickthrough log for matching n-gram features. user clicks on a url in response to a query are extremely useful predictors of the url's relevance to that query. exact match click features tend to suffer from severe data sparsity issues in web ranking. such sparsity is particularly pronounced for new urls or long queries where each distinct query-url pair will rarely occur. to remedy this, we present a set of straightforward yet informative query-url n-gram features that allows for generalization of limited user click data to large amounts of unseen query-url pairs. the method is motivated by techniques leveraged in the nlp community for dealing with unseen words. we find that there are interesting regularities across queries and their preferred destination urls; for example, queries containing "form" tend to lead to clicks on urls containing "pdf". we evaluate our set of new query-url features on a web search ranking task and obtain improvements that are statistically significant at a p-value < 0.0001 level over a strong baseline with exact match clickthrough features.
synchronous tree adjoining machine translation. tree adjoining grammars have well-known advantages, but are typically considered too difficult for practical systems. we demonstrate that, when done right, adjoining improves translation quality without becoming computationally intractable. using adjoining to model optionality allows general translation patterns to be learned without the clutter of endless variations of optional material. the appropriate modifiers can later be spliced in as needed. in this paper, we describe a novel method for learning a type of synchronous tree adjoining grammar and associated probabilities from aligned tree/string training data. we introduce a method of converting these grammars to a weakly equivalent tree transducer for decoding. finally, we show that adjoining results in an end-to-end improvement of +0.8 bleu over a baseline statistical syntax-based mt model on a large-scale arabic/english mt task.
the infinite hmm for unsupervised pos tagging. we extend previous work on fully unsupervised part-of-speech tagging. using a non-parametric version of the hmm, called the infinite hmm (ihmm), we address the problem of choosing the number of hidden states in unsupervised markov models for pos tagging. we experiment with two non-parametric priors, the dirichlet and pitman-yor processes, on the wall street journal dataset using a parallelized implementation of an ihmm inference algorithm. we evaluate the results with a variety of clustering evaluation metrics and achieve equivalent or better performances than previously reported. building on this promising result we evaluate the output of the unsupervised pos tagger as a direct replacement for the output of a fully supervised pos tagger for the task of shallow parsing and compare the two evaluations.
what's in a name? in some languages, grammatical gender. this paper presents an investigation of the relation between words and their gender in two gendered languages: german and romanian. gender is an issue that has long preoccupied linguists and baffled language learners. we verify the hypothesis that gender is dictated by the general sound patterns of a language, and that it goes beyond suffixes or word endings. experimental results on german and romanian nouns show strong support for this hypothesis, as gender prediction can be done with high accuracy based on the form of the words.
the role of named entities in web people search. the ambiguity of person names in the web has become a new area of interest for nlp researchers. this challenging problem has been formulated as the task of clustering web search results (returned in response to a person name query) according to the individual they mention. in this paper we compare the coverage, reliability and independence of a number of features that are potential information sources for this clustering task, paying special attention to the role of named entities in the texts to be clustered. although named entities are used in most approaches, our results show that, independently of the machine learning or clustering algorithm used, named entity recognition and classification per se only make a small contribution to solve the problem.
phrase dependency parsing for opinion mining. in this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. by taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase level. this concept is then implemented for extracting relations between product features and expressions of opinions. experimental evaluations show that the mining task can benefit from phrase dependency parsing.
generalized expectation criteria for bootstrapping extractors using record-text alignment. traditionally, machine learning approaches for information extraction require human annotated data that can be costly and time-consuming to produce. however, in many cases, there already exists a database (db) with schema related to the desired output, and records related to the expected input text. we present a conditional random field (crf) that aligns tokens of a given db record and its realization in text. the crf model is trained using only the available db and unlabeled text with generalized expectation criteria. an annotation of the text induced from inferred alignments is used to train an information extractor. we evaluate our method on a citation extraction task in which alignments between dblp database records and citation texts are used to train an extractor. experimental results demonstrate an error reduction of 35% over a previous state-of-the-art method that uses heuristic alignments.
unbounded dependency recovery for parser evaluation. this paper introduces a new parser evaluation corpus containing around 700 sentences annotated with unbounded dependencies, from seven different grammatical constructions. we run a series of off-the-shelf parsers on the corpus to evaluate how well state-of-the-art parsing technology is able to recover such dependencies. the overall results range from 25% accuracy to 59%. these low scores call into question the validity of using parseval scores as a general measure of parsing capability. we discuss the importance of parsers being able to recover unbounded dependencies, given their relatively low frequency in corpora. we also analyse the various errors made on these constructions by one of the more successful parsers.