waashk/dblp huggingface dataset:
choosing the right translation: a syntactically informed classification approach. one style of multi-engine machine translation architecture involves choosing the best of a set of outputs from different systems. choosing the best translation from an arbitrary set, even in the presence of human references, is a difficult problem; it may prove better to look at mechanisms for making such choices in more restricted contexts. in this paper we take a classification-based approach to choosing between candidates from syntactically informed translations. the idea is that using multiple parsers as part of a classifier could help detect syntactic problems in this context that lead to bad translations; these problems could be detected on either the source side---perhaps sentences with difficult or incorrect parses could lead to bad translations---or on the target side---perhaps the output quality could be measured in a more syntactically informed way, looking for syntactic abnormalities. we show that there is no evidence that the source side information is useful. however, a target-side classifier, when used to identify particularly bad translation candidates, can lead to significant improvements in bleu score. improvements are even greater when combined with existing language and alignment model approaches.
hindi urdu machine transliteration using finite-state transducers. finite-state transducers (fst) can be very efficient to implement inter-dialectal transliteration. we illustrate this on the hindi and urdu language pair. fsts can also be used for translation between surface-close languages. we introduce uit (universal intermediate transcription) for the same pair on the basis of their common phonetic repository in such a way that it can be extended to other languages like arabic, chinese, english, french, etc. we describe a transliteration model based on fst and uit, and evaluate it on hindi and urdu corpora.
tera-scale translation models via pattern matching. translation model size is growing at a pace that outstrips improvements in computing power, and this hinders research on many interesting models. we show how an algorithmic scaling technique can be used to easily handle very large models. using this technique, we explore several large model variants and show an improvement 1.4 bleu on the nist 2006 chinese-english task. this opens the door for work on a variety of models that are much less constrained by computational limitations.
class-driven attribute extraction. we report on the large-scale acquisition of class attributes with and without the use of lists of representative instances, as well as the discovery of unary attributes, such as typically expressed in english through prenominal adjectival modification. our method employs a system based on compositional language processing, as applied to the british national corpus. experimental results suggest that document-based, open class attribute extraction can produce results of comparable quality as those obtained using web query logs, indicating the utility of exploiting explicit occurrences of class labels in text.
metric learning for synonym acquisition. the distance or similarity metric plays an important role in many natural language processing (nlp) tasks. previous studies have demonstrated the effectiveness of a number of metrics such as the jaccard coefficient, especially in synonym acquisition. while the existing metrics perform quite well, to further improve performance, we propose the use of a supervised machine learning algorithm that fine-tunes them. given the known instances of similar or dissimilar words, we estimated the parameters of the mahalanobis distance. we compared a number of metrics in our experiments, and the results show that the proposed metric has a higher mean average precision than other metrics.
a classifier-based approach to preposition and determiner error correction in l2 english. in this paper, we present an approach to the automatic identification and correction of preposition and determiner errors in non-native (l2) english writing. we show that models of use for these parts of speech can be learned with an accuracy of 70.06% and 92.15% respectively on l1 text, and present first results in an error detection task for l2 writing.
event frame extraction based on a gene regulation corpus. this paper describes the supervised acquisition of semantic event frames based on a corpus of biomedical abstracts, in which the biological process of e. coli gene regulation has been linguistically annotated by a group of biologists in the ec research project "bootstrep". gene regulation is one of the rapidly advancing areas for which information extraction could boost research. event frames are an essential linguistic resource for extraction of information from biological literature. this paper presents a specification for linguistic-level annotation of gene regulation events, followed by novel methods of automatic event frame extraction from text. the event frame extraction performance has been evaluated with 10-fold cross validation. the experimental results show that a precision of nearly 50% and a recall of around 20% are achieved. since the goal of this paper is event frame extraction, rather than event instance extraction, the issue of low recall could be solved by applying the methods to a larger-scale corpus.
the ups and downs of preposition error detection in esl writing. in this paper we describe a methodology for detecting preposition errors in the writing of non-native english speakers. our system performs at 84% precision and close to 19% recall on a large set of student essays. in addition, we address the problem of annotation and evaluation in this domain by showing how current approaches of using only one rater can skew system evaluation. we present a sampling approach to circumvent some of the issues that complicate evaluation of error detection systems.
learning reliable information for dependency parsing adaptation. in this paper, we focus on the adaptation problem that has a large labeled data in the source domain and a large but unlabeled data in the target domain. our aim is to learn reliable information from unlabeled target domain data for dependency parsing adaptation. current state-of-the-art statistical parsers perform much better for shorter dependencies than for longer ones. thus we propose an adaptation approach by learning reliable information on shorter dependencies in an unlabeled target data to help parse longer distance words. the unlabeled data is parsed by a dependency parser trained on labeled source domain data. the experimental results indicate that our proposed approach outperforms the baseline system, and is better than current state-of-the-art adaptation techniques.
measuring topic homogeneity and its application to dictionary-based word sense disambiguation. the use of topical features is abundant in natural language processing (nlp), a major example being in dictionary-based word sense disambiguation (wsd). yet previous research does not attempt to measure the level of topic cohesion in documents, despite assertions of its effects. this paper introduces a quantitative measure of topic homogeneity using a range of nlp resources and not requiring prior knowledge of correct senses. evaluation is performed firstly by using the wordnet::domains package to create word-sets with varying levels of homogeneity and comparing our results with those expected. additionally, to evaluate each measure's potential value, the homogeneity results are correlated against those of 3 co-occurrence/dictionary-based wsd techniques, tested on 1040 semcor and senseval sub-documents. many low-moderate correlations are found to exist with several in the moderate range (above .40). these correlations surpass polysemy and senseentropy, the 2 most cited factors affecting wsd. finally, a combined homogeneity measure achieves correlations of up to .52.
automatic generation of parallel treebanks. the need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. this is true especially for parallel treebanks, of which very few exist. the ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. in this paper we introduce a novel platform for fast and robust automatic generation of parallel treebanks. the software we have developed based on this platform has been shown to handle large data sets. we also present evaluation results demonstrating the quality of the derived treebanks and discuss some possible modifications and improvements that can lead to even better results. we expect the presented platform to help boost research in the field of data-oriented machine translation and lead to advancements in other fields where parallel treebanks can be employed.
improving statistical machine translation using lexicalized rule selection. this paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (smt). we build maximum entropy (maxent) models which combine rich context information for selecting translation rules during decoding. we successfully integrate the maxent-based rule selection models into the state-of-the-art syntax-based smt model. experiments show that our lexicalized approach for rule selection achieves statistically significant improvements over the state-of-the-art smt system.
generating chinese couplets using a statistical mt approach. part of the unique cultural heritage of china is the game of chinese couplets (du&igrave;li&aacute;n). one person challenges the other person with a sentence (first sentence). the other person then replies with a sentence (second sentence) equal in length and word segmentation, in a way that corresponding words in the two sentences match each other by obeying certain constraints on semantic, syntactic, and lexical relatedness. this task is viewed as a difficult problem in ai and has not been explored in the research community. in this paper, we regard this task as a kind of machine translation process. we present a phrase-based smt approach to generate the second sentence. first, the system takes as input the first sentence, and generates as output an n-best list of proposed second sentences, using a phrase-based smt decoder. then, a set of filters is used to remove candidates violating linguistic constraints. finally, a ranking svm is applied to rerank the candidates. a comprehensive evaluation, using both human judgments and bleu scores, has been conducted, and the results demonstrate that this approach is very successful.
detecting multiple facets of an event using graph-based unsupervised methods. we propose a new unsupervised method for topic detection that automatically identifies the different facets of an event. we use pointwise kullback-leibler divergence along with the jaccard coefficient to build a topic graph which represents the community structure of the different facets. the problem is formulated as a weighted set cover problem with dynamically varying weights. the algorithm is domain-independent and generates a representative set of informative and discriminative phrases that cover the entire event. we evaluate this algorithm on a large collection of blog postings about different news events and report promising results.
collabrank: towards a collaborative approach to single-document keyphrase extraction. previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are considered independent of each other. this paper proposes a novel approach named collabrank to collaborative single-document keyphrase extraction by making use of mutual influences of multiple documents within a cluster context. collabrank is implemented by first employing the clustering algorithm to obtain appropriate document clusters, and then using the graph-based ranking algorithm for collaborative single-document keyphrase extraction within each cluster. experimental results demonstrate the encouraging performance of the proposed approach. different clustering algorithms have been investigated and we find that the system performance relies positively on the quality of document clusters.
exploiting graph structure for accelerating the calculation of shortest paths in wordnets. this paper presents an approach for substantially reducing the time needed to calculate the shortest paths between all concepts in a wordnet. the algorithm exploits the unique "star-like" topology of wordnets to cut down on time-expensive calculations performed by algorithms to solve the all-pairs shortest path problem in general graphs. the algorithm was applied to two wordnets of two different languages: princeton wordnet (fellbaum, 1998) for english, and germanet (kunze and lemnitzer, 2002), the german language wordnet. for both wordnets, the time needed for finding all shortest paths was brought down from several days to a matter of minutes.
chinese dependency parsing with large scale automatically constructed case structures. this paper proposes an approach using large scale case structures, which are automatically constructed from both a small tagged corpus and a large raw corpus, to improve chinese dependency parsing. the case structure proposed in this paper has two characteristics: (1) it relaxes the predicate of a case structure to be all types of words which behaves as a head; (2) it is not categorized by semantic roles but marked by the neighboring modifiers attached to a head. experimental results based on penn chinese treebank show the proposed approach achieved 87.26% on unlabeled attachment score, which significantly outperformed the baseline parser without using case structures.
statistical anaphora resolution in biomedical texts. this paper presents a probabilistic model for resolution of non-pronominal anaphora in biomedical texts. the model seeks to find the antecedents of anaphoric expressions, both coreferent and associative ones, and also to identify discourse-new expressions. we consider only the noun phrases referring to biomedical entities. the model reaches state-of-the art performance: 56--69% precision and 54--67% recall on coreferent cases, and reasonable performance on different classes of associative cases.
a classification of dialogue actions in tutorial dialogue. in this paper we present a taxonomy of dialogue moves which describe the actions that students and tutors perform in tutorial dialogue. we are motivated by the need for a categorisation of such actions in order to develop computational models for tutorial dialogue. as such, we build both on existing work on dialogue move categorisation for tutorial dialogue as well as dialogue taxonomies for general dialogue. our taxonomy has been prepared by analysing a corpus of tutorial dialogues on mathematical theorem proving. we also detail an annotation experiment in which we apply the taxonomy and discuss idiosyncrasies in the data which influence the decisions in the dialogue move classification.
source language markers in europarl translations. this paper shows that it is very often possible to identify the source language of medium-length speeches in the europarl corpus on the basis of frequency counts of word n-grams (87.2%--96.7% accuracy depending on classification method). the paper also examines in detail which positive markers are most powerful and identifies a number of linguistic aspects as well as culture- and domain-related ones.
linguistically-based sub-sentential alignment for terminology extraction from a bilingual automotive corpus. we present a sub-sentential alignment system that links linguistically motivated phrases in parallel texts based on lexical correspondences and syntactic similarity. we compare the performance of our sub-sentential alignment system with different symmetrization heuristics that combine the giza++ alignments of both translation directions. we demonstrate that the aligned linguistically motivated phrases are a useful means to extract bilingual terminology and more specifically complex multiword terms.
learning entailment rules for unary templates. most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules - entailment rules between templates with a single variable. in this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed methods with a binary rule learning method. the results show that the learned unary rule-sets outperform the binary rule-set. in addition, a novel directional similarity measure for learning entailment, termed balanced-inclusion, is the best performing measure.
improving alignments for better confusion networks for combining machine translation systems. the state-of-the-art system combination method for machine translation (mt) is the word-based combination using confusion networks. one of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. in this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. we demonstrate that combination with the new alignment technique yields up to 2.9 bleu point improvement over the best input system and up to 1.3 bleu point improvement over a state-of-the-art combination method on two different language pairs.
the effect of syntactic representation on semantic role labeling. almost all automatic semantic role labeling (srl) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. this makes the choice of syntactic representation an essential design decision. in this paper, we study the influence of syntactic representation on the performance of srl systems. specifically, we compare constituent-based and dependency-based representations for srl of english in the framenet paradigm. contrary to previous claims, our results demonstrate that the systems based on dependencies perform roughly as well as those based on constituents: for the argument classification task, dependency-based systems perform slightly higher on average, while the opposite holds for the argument identification task. this is remarkable because dependency parsers are still in their infancy while constituent parsing is more mature. furthermore, the results show that dependency-based semantic role classifiers rely less on lexicalized features, which makes them more robust to domain changes and makes them learn more efficiently with respect to the amount of training data.
classifying what-type questions by head noun tagging. classifying what-type questions into proper semantic categories is found more challenging than classifying other types in question answering systems. in this paper, we propose to classify what-type questions by head noun tagging. the approach highlights the role of head nouns as the category discriminator of what-type questions. to reduce the semantic ambiguities of head noun, we integrate local syntactic feature, semantic feature and category dependency among adjacent nouns with conditional random fields (crfs). experiments on standard question classification data set show that the approach achieves state-of-the-art performances.
modeling the structure and dynamics of the consonant inventories: a complex network approach. we study the self-organization of the consonant inventories through a complex network approach. we observe that the distribution of occurrence as well as co-occurrence of the consonants across languages follow a power-law behavior. the co-occurrence network of consonants exhibits a high clustering coefficient. we propose four novel synthesis models for these networks (each of which is a refinement of the earlier) so as to successively match with higher accuracy (a) the above mentioned topological properties as well as (b) the linguistic property of feature economy exhibited by the consonant inventories. we conclude by arguing that a possible interpretation of this mechanism of network growth is the process of child language acquisition. such models essentially increase our understanding of the structure of languages that is influenced by their evolutionary dynamics and this, in turn, can be extremely useful for building future nlp applications.
good neighbors make good senses: exploiting distributional similarity for unsupervised wsd. we present an automatic method for senselabeling of text in an unsupervised manner. the method makes use of distributionally similar words to derive an automatically labeled training set, which is then used to train a standard supervised classifier for distinguishing word senses. experimental results on the senseval-2 and senseval-3 datasets show that our approach yields significant improvements over state-of-the-art unsupervised methods, and is competitive with supervised ones, while eliminating the annotation cost.
a concept-centered approach to noun-compound interpretation. a noun-compound is a compressed proposition that requires an audience to recover the implicit relationship between two concepts that are expressed as nouns. listeners recover this relationship by considering the most typical relations afforded by each concept. these relational possibilities are evident at a linguistic level in the syntagmatic patterns that connect nouns to the verbal actions that act upon, or are facilitated by, these nouns. we present a model of noun-compound interpretation that first learns the relational possibilities for individual nouns from corpora, and which then uses these to hypothesize about the most likely relationship that underpins a noun compound.
instance-based ontology population exploiting named-entity substitution. we present an approach to ontology population based on a lexical substitution technique. it consists in estimating the plausibility of sentences where the named entity to be classified is substituted with the ones contained in the training data, in our case, a partially populated ontology. plausibility is estimated by using web data, while the classification algorithm is instance-based. we evaluated our method on two different ontology population tasks. experiments show that our solution is effective, outperforming existing methods, and it can be applied to practical ontology population problems.
classifying chart cells for quadratic complexity context-free inference. in this paper, we consider classifying word positions by whether or not they can either start or end multi-word constituents. this provides a mechanism for "closing" chart cells during context-free inference, which is demonstrated to improve efficiency and accuracy when used to constrain the well-known charniak parser. additionally, we present a method for "closing" a sufficient number of chart cells to ensure quadratic worst-case complexity of context-free inference. empirical results show that this o(n2) bound can be achieved without impacting parsing accuracy.
sentence compression beyond word deletion. in this paper we generalise the sentence compression task. rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. we present a new corpus that is suited to our task and a discriminative tree-to-tree transduction model that can naturally account for structural and lexical mismatches. the model incorporates a novel grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression specific loss functions.