waashk/dblp huggingface dataset:
a probabilistic model for associative anaphora resolution. this paper proposes a probabilistic model for associative anaphora resolution in japanese. associative anaphora is a type of bridging anaphora, in which the anaphor and its antecedent are not coreferent. our model regards associative anaphora as a kind of zero anaphora and resolves it in the same manner as zero anaphora resolution using automatically acquired lexical knowledge. experimental results show that our model resolves associative anaphora with good performance and the performance is improved by resolving it simultaneously with zero anaphora.
cube pruning as heuristic search. cube pruning is a fast inexact method for generating the items of a beam decoder. in this paper, we show that cube pruning is essentially equivalent to a* search on a specific search space with specific heuristics. we use this insight to develop faster and exact variants of cube pruning.
unsupervised tokenization for machine translation. training a statistical machine translation starts with tokenizing a parallel corpus. some languages such as chinese do not incorporate spacing in their writing system, which creates a challenge for tokenization. moreover, morphologically rich languages such as korean present an even bigger challenge, since optimal token boundaries for machine translation in these languages are often unclear. both rule-based solutions and statistical solutions are currently used. in this paper, we present unsupervised methods to solve tokenization problem. our methods incorporate information available from parallel corpus to determine a good tokenization for machine translation.
graphical models over multiple strings. we study graphical modeling in the case of string-valued random variables. whereas a weighted finite-state transducer can model the probabilistic relationship between two strings, we are interested in building up joint models of three or more strings. this is needed for inflectional paradigms in morphology, cognate modeling or language reconstruction, and multiple-string alignment. we propose a markov random field in which each factor (potential function) is a weighted finite-state machine, typically a transducer that evaluates the relationship between just two of the strings. the full joint distribution is then a product of these factors. though decoding is actually undecidable in general, we can still do efficient joint inference using approximate belief propagation; the necessary computations and messages are all finite-state. we demonstrate the methods by jointly predicting morphological forms.
a rich feature vector for protein-protein interaction extraction from multiple corpora. because of the importance of protein-protein interaction (ppi) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and ppi. since no single corpus is large enough to saturate a machine learning system, it is necessary to learn from multiple different corpora. in this paper, we propose a solution to this challenge. we designed a rich feature vector, and we applied a support vector machine modified for corpus weighting (svm-cw) to complete the task of multiple corpora ppi extraction. the rich feature vector, made from multiple useful kernels, is used to express the important information for ppi extraction, and the system with our feature vector was shown to be both faster and more accurate than the original kernel-based system, even when using just a single corpus. svm-cw learns from one corpus, while using other corpora for support. svm-cw is simple, but it is more effective than other methods that have been successfully applied to other nlp tasks earlier. with the feature vector and svm-cw, our system achieved the best performance among all state-of-the-art ppi extraction systems reported so far.
sentiment analysis of conditional sentences. this paper studies sentiment analysis of conditional sentences. the aim is to determine whether opinions expressed on different topics in a conditional sentence are positive, negative or neutral. conditional sentences are one of the commonly used language constructs in text. in a typical document, there are around 8% of such sentences. due to the condition clause, sentiments expressed in a conditional sentence can be hard to determine. for example, in the sentence, if your nokia phone is not good, buy this great samsung phone, the author is positive about "samsung phone" but does not express an opinion on "nokia phone" (although the owner of the "nokia phone" may be negative about it). however, if the sentence does not have "if', the first clause is clearly negative. although "if' commonly signifies a conditional sentence, there are many other words and constructs that can express conditions. this paper first presents a linguistic analysis of such sentences, and then builds some supervised learning models to determine if sentiments expressed on different topics in a conditional sentence are positive, negative or neutral. experimental results on conditional sentences from 5 diverse domains are given to demonstrate the effectiveness of the proposed approach.
a comparison of model free versus model intensive approaches to sentence compression. this work introduces a model free approach to sentence compression, which grew out of ideas from nomoto (2008), and examines how it compares to a state-of-art model intensive approach known as tree-to-tree transducer, or t3 (cohn and lapata, 2008). it is found that a model free approach significantly outperforms t3 on the particular data we created from the internet. we also discuss what might have caused t3's poor performance.
human-competitive tagging using automatic keyphrase extraction. this paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. first, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "maui", that utilizes semantic information extracted from wikipedia. maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
toward completeness in concept extraction and classification. many algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. however, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. harvesting without focusing on a specific conceptual area may deliver large numbers of terms, but they are scattered over an immense concept space, making recall judgments impossible. regarding precision, simply judging the correctness of terms and their individual classification links may provide high scores, but this doesn't help with the eventual assembly of terms into a single coherent taxonomy. furthermore, since there is no correct and complete gold standard to measure against, most work invents some ad hoc evaluation measure. we present an algorithm that is more precise and complete than previous ones for identifying from web text just those concepts 'below' a given seed term. comparing the results to wordnet, we find that the algorithm misses terms, but also that it learns many new terms not in wordnet, and that it classifies them in ways acceptable to humans but different from wordnet.
joint learning of preposition senses and semantic roles of prepositional phrases. the sense of a preposition is related to the semantics of its dominating prepositional phrase. knowing the sense of a preposition could help to correctly classify the semantic role of the dominating prepositional phrase and vice versa. in this paper, we propose a joint probabilistic model for word sense disambiguation of prepositions and semantic role labeling of prepositional phrases. our experiments on the propbank corpus show that jointly learning the word sense and the semantic role leads to an improvement over state-of-the-art individual classifier models on the two tasks.
natural language generation with tree conditional random fields. this paper presents an effective method for generating natural language sentences from their underlying meaning representations. the method is built on top of a hybrid tree representation that jointly encodes both the meaning representation as well as the natural language in a tree structure. by using a tree conditional random field on top of the hybrid tree representation, we are able to explicitly model phrase-level dependencies amongst neighboring natural language phrases and meaning representation components in a simple and natural way. we show that the additional dependencies captured by the tree conditional random field allows it to perform better than directly inverting a previously developed hybrid tree semantic parser. furthermore, we demonstrate that the model performs better than a previous state-of-the-art natural language generation model. experiments are performed on two benchmark corpora with standard automatic evaluation metrics.
nested named entity recognition. many named entities contain other named entities inside them. despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recognition, but due to technological, rather than ideological reasons. in this paper, we present a new technique for recognizing nested named entities, by using a discriminative constituency parser. to train the model, we transform each sentence into a tree, with constituents for each named entity (and no other syntactic structure). we present results on both newspaper and biomedical corpora which contain nested named entities. in three out of four sets of experiments, our model outperforms a standard semi-crf on the more traditional top-level entities. at the same time, we improve the overall f-score by up to 30% over the flat model, which is unable to recover any nested entities.
a structural support vector method for extracting contexts and answers of questions from online forums. this paper addresses the issue of extracting contexts and answers of questions from post discussion of online forums. we propose a novel and unified model by customizing the structural support vector machine method. our customization has several attractive properties: (1) it gives a comprehensive graphical representation of thread discussion. (2) it designs special inference algorithms instead of general-purpose ones. (3) it can be readily extended to different task preferences by varying loss functions. experimental results on a real data set show that our methods are both promising and flexible.
bilingually-constrained (monolingual) shift-reduce parsing. jointly parsing two languages has been shown to improve accuracies on either or both sides. however, its search space is much bigger than the monolingual case, forcing existing approaches to employ complicated modeling and crude approximations. here we propose a much simpler alternative, bilingually-constrained monolingual parsing, where a source-language parser learns to exploit reorderings as additional observation, but not bothering to build the target-side tree as well. we show specifically how to enhance a shift-reduce dependency parser with alignment features to resolve shift-reduce conflicts. experiments on the bilingual portion of chinese treebank show that, with just 3 bilingual features, we can improve parsing accuracies by 0.6% (absolute) for both english and chinese over a state-of-the-art baseline, with negligible (~6%) efficiency overhead, thus much faster than biparsing.
lattice-based system combination for statistical machine translation. current system combination methods usually use confusion networks to find consensus translations among different systems. requiring one-to-one mappings between the words in candidate translations, confusion networks have difficulty in handling more general situations in which several words are connected to another several words. instead, we propose a lattice-based system combination model that allows for such phrase alignments and uses lattices to encode all candidate translations. experiments show that our approach achieves significant improvements over the state-of-the-art baseline system on chinese-to-english translation test sets.
classifier combination for contextual idiom detection without labelled data. we propose a novel unsupervised approach for distinguishing literal and non-literal use of idiomatic expressions. our model combines an unsupervised and a supervised classifier. the former bases its decision on the cohesive structure of the context and labels training data for the latter, which can then take a larger feature space into account. we show that a combination of both classifiers leads to significant improvements over using the unsupervised classifier alone.
topic-wise, sentiment-wise, or otherwise? identifying the hidden dimension for unsupervised text classification. while traditional work on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author's mood, gender, age, or sentiment. without knowing the user's intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. to address this problem, we propose a novel way of incorporating user feedback into a clustering algorithm, which allows a user to easily specify the dimension along which she wants the data points to be clustered via inspecting only a small number of words. this distinguishes our method from existing ones, which typically require a large amount of effort on the part of humans in the form of document annotation or interactive construction of the feature space. we demonstrate the viability of our method on several challenging sentiment datasets.
adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification. polarity lexicons have been a valuable resource for sentiment analysis and opinion mining. there are a number of such lexical resources available, but it is often suboptimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. in this paper, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to reflect the characteristics of the data more directly. in particular, our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item (positive, neutral, negative, or negator) for the given domain. experimental results show that our lexicon adaptation technique improves the performance of fine-grained polarity classification.
eeg responds to conceptual stimuli and corpus semantics. mitchell et al. (2008) demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fmri. this could be a very powerful technique for evaluating conceptual models extracted from corpora; however, fmri is expensive and imposes strong constraints on data collection. following on experiments that demonstrated that eeg activation patterns encode enough information to discriminate broad conceptual categories, we show that corpus-based semantic representations can predict eeg activation patterns with significant accuracy, and we evaluate the relative performance of different corpus-models on this task.
descriptive and empirical approaches to capturing underlying dependencies among parsing errors. in this paper, we provide descriptive and empirical approaches to effectively extracting underlying dependencies among parsing errors. in the descriptive approach, we define some combinations of error patterns and extract them from given errors. in the empirical approach, on the other hand, we re-parse a sentence with a target error corrected and observe errors corrected together. experiments on an hpsg parser show that each of these approaches can clarify the dependencies among individual errors from each point of view. moreover, the comparison between the results of the two approaches shows that combining these approaches can achieve a more detailed error analysis.
using the web for language independent spellchecking and autocorrection. we have designed, implemented and evaluated an end-to-end system spellchecking and autocorrection system that does not require any manually annotated training data. the world wide web is used as a large noisy corpus from which we infer knowledge about misspellings and word usage. this is used to build an error model and an n-gram language model. a small secondary set of news texts with artificially inserted misspellings are used to tune confidence classifiers. because no manual annotation is required, our system can easily be instantiated for new languages. when evaluated on human typed data with real misspellings in english and german, our web-based systems outperform baselines which use candidate corrections based on hand-curated dictionaries. our system achieves 3.8% total error rate in english. we show similar improvements in preliminary results on artificial data for russian and arabic.
extending statistical machine translation with discriminative and trigger-based lexicon models. in this work, we propose two extensions of standard word lexicons in statistical machine translation: a discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends ibm model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. the models capture dependencies that go beyond the scope of conventional smt models such as phrase-and language models. we show that the models improve translation quality by 1% in bleu over a competitive baseline on a large-scale task.
entity extraction via ensemble semantics. combining information extraction systems yields significantly higher quality resources than each system in isolation. in this paper, we generalize such a mixing of sources and features in a framework called ensemble semantics. we show very large gains in entity extraction by combining state-of-the-art distributional and pattern-based systems with a large set of features from a webcrawl, query logs, and wikipedia. experimental results on a web-scale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29% gain) compared with the current state of the art.
a study on the semantic relatedness of query and document terms in information retrieval. the use of lexical semantic knowledge in information retrieval has been a field of active study for a long time. collaborative knowledge bases like wikipedia and wiktionary, which have been applied in computational methods only recently, offer new possibilities to enhance information retrieval. in order to find the most beneficial way to employ these resources, we analyze the lexical semantic relations that hold among query and document terms and compare how these relations are represented by a measure for semantic relatedness. we explore the potential of different indicators of document relevance that are based on semantic relatedness and compare the characteristics and performance of the knowledge bases wikipedia, wiktionary and wordnet.
using morphological and syntactic structures for chinese opinion analysis. this paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. chinese words are classified into eight morphological types by two proposed classifiers, crf classifier and svm classifier. experiments show that the injection of morphological information improves the performance of the word polarity detection. to utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. experiments show that considering structural trios is useful for sentence opinion analysis. the best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.
a joint language model with fine-grain syntactic tags. we present a scalable joint language model designed to utilize fine-grain syntactic tags. we discuss challenges such a design faces and describe our solutions that scale well to large tagsets and corpora. we advocate the use of relatively simple tags that do not require deep linguistic knowledge of the language but provide more structural information than pos tags and can be derived from automatically generated parse trees - a combination of properties that allows easy adoption of this model for new languages. we propose two fine-grain tagsets and evaluate our model using these tags, as well as pos tags and superarv tags in a speech recognition task and discuss future directions.
employing the centering theory in pronoun resolution from the semantic perspective. in this paper, we employ the centering theory in pronoun resolution from the semantic perspective. first, diverse semantic role features with regard to different predicates in a sentence are explored. moreover, given a pronominal anaphor, its relative ranking among all the pronouns in a sentence, according to relevant semantic role information and its surface position, is incorporated. in particular, the use of both the semantic role features and the relative pronominal ranking feature in pronoun resolution is guided by extending the centering theory from the grammatical level to the semantic level in tracking the local discourse focus. finally, detailed pronominal subcategory features are incorporated to enhance the discriminative power of both the semantic role features and the relative pronominal ranking feature. experimental results on the ace 2003 corpus show that the centering-motivated features contribute much to pronoun resolution.
a relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web. semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychology. accurate measurement of semantic similarity between words is essential for various tasks such as, document clustering, information retrieval, and synonym extraction. we propose a novel model of semantic similarity using the semantic relations that exist among words. given two words, first, we represent the semantic relations that hold between those words using automatically extracted lexical pattern clusters. next, the semantic similarity between the two words is computed using a mahalanobis distance measure. we compare the proposed similarity measure against previously proposed semantic similarity measures on miller-charles benchmark dataset and wordsimilarity-353 collection. the proposed method outperforms all existing web-based semantic similarity measures, achieving a pearson correlation coefficient of 0.867 on the millet-charles dataset.
feature-rich translation by quasi-synchronous lattice parsing. we present a machine translation framework that can incorporate arbitrary features of both input and output sentences. the core of the approach is a novel decoder based on lattice parsing with quasi-synchronous grammar (smith and eisner, 2006), a syntactic formalism that does not require source and target trees to be isomorphic. using generic approximate dynamic programming techniques, this decoder can handle "non-local" features. similar approximate inference techniques support efficient parameter estimation with hidden variables. we use the decoder to conduct controlled experiments on a german-to-english translation task, to compare lexical phrase, syntax, and combined models, and to measure effects of various restrictions on non-isomorphism.