waashk/dblp huggingface dataset:
a composite kernel to extract relations between entities with both flat and structured features. this paper proposes a novel composite kernel for relation extraction. the composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. the motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. evaluation on the ace corpus shows that our method outperforms the previous best-reported methods and significantly out-performs previous two dependency tree kernels for relation extraction.
extracting relations with integrated information using kernel methods. entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. this paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. each source of information is represented by kernel functions. then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. we present an evaluation of these methods on the 2004 ace relation detection task, using support vector machines, and show that each level of syntactic processing contributes useful information for this task. when evaluated on the official test data, our approach produced very competitive ace value scores. we also compare the svm with knn on different kernels.
bitam: bilingual topic admixture models for word alignment. we propose a novel bilingual topical admixture (bitam) formalism for word alignment in statistical machine translation. under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitute a mixture of hidden topics; each word-pair follows a topic-specific bilingual translation model. three bitam models are proposed to capture topic sharing at different levels of linguistic granularity (i.e., at the sentence or word levels). these models enable word-alignment process to leverage topical contents of document-pairs. efficient variational approximation algorithms are designed for inference and parameter estimation. with the inferred latent topics, bitam models facilitate coherent pairing of bilingual linguistic entities that share common topical aspects. our preliminary experiments show that the proposed models improve word alignment accuracy, and lead to better translation quality.
digesting virtual "geek" culture: the summarization of technical internet relay chats. this paper describes a summarization system for technical chats and emails on the linux kernel. to reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. a resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion.
word association and mi-trigger-based language modeling. there exists strong word association in natural language. based on mutual information, this paper proposes a new mi-trigger-based modeling approach to capture the preferred relationships between words over a short or long distance. both the distance-independent(di) and distance-dependent(dd) mi-trigger-based models are constructed within a window. it is found that proper mi-trigger modeling is superior to word bigram model and the dd mi-trigger models have better performance than the di mi-trigger models for the same window size. it is also found that the number of the trigger pairs in an mi-trigger model can be kept to a reasonable size without losing too much of its modeling power. finally, it is concluded that the preferred relationships between words are useful to language disambiguation and can be modeled efficiently by the mi-trigger-based modeling approach.
automatically assessing the post quality in online discussions on software. assessing the quality of user generated content is an important problem for many web forums. while quality is currently assessed manually, we propose an algorithm to assess the quality of forum posts automatically and test it on data provided by nabble.com. we use state-of-the-art classification techniques and experiment with five feature classes: surface, lexical, syntactic, forum specific and similarity features. we achieve an accuracy of 89% on the task of automatically assessing post quality in the software domain using forum specific features. without forum specific features, we achieve an accuracy of 82%.
named entity recognition using an hmm-based chunk tagger. this paper proposes a hidden markov model (hmm) and an hmm-based chunk tagger, from which a named entity (ne) recognition (ner) system is built to recognize and classify names, times and numerical quantities. through the hmm, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. in this way, the ner problem can be resolved effectively. evaluation of our system on muc-6 and muc-7 english ne tasks achieves f-measures of 96.6% and 94.1% respectively. it shows that the performance is significantly better than reported by any other machine-learning system. moreover, the performance is even consistently better than those based on handcrafted rules.
generating usable formats for metadata and annotations in a large meeting corpus. the ami meeting corpus is now publicly available, including manual annotation files generated in the nxt xml format, but lacking explicit metadata for the 171 meetings of the corpus. to increase the usability of this important resource, a representation format based on relational databases is proposed, which maximizes informativeness, simplicity and reusability of the metadata and annotations. the annotation files are converted to a tabular format using an easily adaptable xslt-based mechanism, and their consistency is verified in the process. metadata files are generated directly in the imdi xml format from implicit information, and converted to tabular format using a similar procedure. the results and tools will be freely available with the ami corpus. sharing the metadata using the open archives network will contribute to increase the visibility of the ami corpus.
a hybrid approach to word segmentation and pos tagging. in this paper, we present a hybrid method for word segmentation and pos tagging. the target languages are those in which word boundaries are ambiguous, such as chinese and japanese. in the method, word-based and character-based processing is combined, and word segmentation and pos tagging are conducted simultaneously. experimental results on multiple corpora show that the integrated method has high accuracy.
an hmm-based approach to automatic phrasing for mandarin text-to-speech synthesis. automatic phrasing is essential to mandarin text-to-speech synthesis. we select word format as target linguistic feature and propose an hmm-based approach to this issue. then we define four states of prosodic positions for each word when employing a discrete hidden markov model. the approach achieves high accuracy of roughly 82%, which is very close to that from manual labeling. our experimental results also demonstrate that this approach has advantages over those part-of-speech-based ones.
an approximate approach for training polynomial kernel svms in linear time. kernel methods such as support vector machines (svms) have attracted a great deal of popularity in the machine learning and natural language processing (nlp) communities. polynomial kernel svms showed very competitive accuracy in many nlp problems, like part-of-speech tagging and chunking. however, these methods are usually too inefficient to be applied to large dataset and real time purpose. in this paper, we propose an approximate method to analogy polynomial kernel with efficient data mining approaches. to prevent exponential-scaled testing time complexity, we also present a new method for speeding up svm classifying which does independent to the polynomial degree d. the experimental results showed that our method is 16.94 and 450 times faster than traditional polynomial kernel in terms of training and testing respectively.
the effect of translation quality in mt-based cross-language information retrieval. this paper explores the relationship between the translation quality and the retrieval effectiveness in machine translation (mt) based cross-language information retrieval (clir). to obtain mt systems of different translation quality, we degrade a rule-based mt system by decreasing the size of the rule base and the size of the dictionary. we use the degraded mt systems to translate queries and submit the translated queries of varying quality to the ir system. retrieval effectiveness is found to correlate highly with the translation quality of the queries. we further analyze the factors that affect the retrieval effectiveness. title queries are found to be preferred in mt-based clir. in addition, dictionary-based degradation is shown to have stronger impact than rule-based degradation in mt-based clir.
maximum entropy based restoration of arabic diacritics. short vowels and other diacritics are not part of written arabic scripts. exceptions are made for important political and religious texts and in scripts for beginning students of arabic. script without diacritics have considerable ambiguity because many words with different diacritic patterns appear identical in a diacritic-less setting. we propose in this paper a maximum entropy approach for restoring diacritics in a document. the approach can easily integrate and make effective use of diverse types of information; the model we propose integrates a wide array of lexical, segment-based and part-of-speech tag features. the combination of these feature types leads to a state-of-the-art diacritization model. using a publicly available corpus (ldc's arabic treebank part 3), we achieve a diacritic error rate of 5.1%, a segment error rate 8.5%, and a word error rate of 17.3%. in case-ending-less setting, we obtain a diacritic error rate of 2.2%, a segment error rate 4.0%, and a word error rate of 7.2%.
a practical classification of multiword expressions. the paper proposes a methodology for dealing with multiword expressions in natural language processing applications. it provides a practically justified taxonomy of such units, and suggests the ways in which the individual classes can be processed computationally. while the study is currently limited to polish and english, we believe our findings can be successfully employed in the processing of other languages, with emphasis on inflectional ones.
rethinking chinese word segmentation: tokenization, character classification, or wordbreak identification. this paper addresses two remaining challenges in chinese word segmentation. the challenge in hlt is to find a robust segmentation method that requires no prior lexical knowledge and no extensive training to adapt to new types of data. the challenge in modelling human cognition and acquisition it to segment words efficiently without using knowledge of wordhood. we propose a radical method of word segmentation to meet both challenges. the most critical concept that we introduce is that chinese word segmentation is the classification of a string of character-boundaries (cb's) into either word-boundaries (wb's) and non-word-boundaries. in chinese, cb's are delimited and distributed in between two characters. hence we can use the distributional properties of cb among the background character strings to predict which cb's are wb's.
mapping concrete entities from parole-simple-clips to italwordnet: methodology and results. this paper describes a work in progress aiming at linking the two largest italian lexical-semantic databases italwordnet and parole-simple-clips. the adopted linking methodology, the software tool devised and implemented for this purpose and the results of the first mapping phase regarding 1storderentities are illustrated here.
construction of domain dictionary for fundamental vocabulary. for natural language understanding, it is essential to reveal semantic relations between words. to date, only the is-a relation has been publicly available. toward deeper natural language understanding, we semi-automatically constructed the domain dictionary that represents the domain relation between japanese fundamental words. this is the first japanese domain resource that is fully available. besides, our method does not require a document collection, which is indispensable for keyword extraction techniques but is hard to obtain. as a task-based evaluation, we performed blog categorization. also, we developed a technique for estimating the domain of unknown words.
exploration of term dependence in sentence retrieval. this paper focuses on the exploration of term dependence in the application of sentence retrieval. the adjacent terms appearing in query are assumed to be related with each other. these assumed dependences among query terms will be further validated for each sentence and sentences, which present strong syntactic relationship among query terms, are considered more relevant. experimental results have fully demonstrated the promising of the proposed models in improving sentence retrieval effectiveness.
deriving an ambiguous word's part-of-speech distribution from unannotated text. a distributional method for part-of-speech induction is presented which, in contrast to most previous work, determines the part-of-speech distribution of syntactically ambiguous words without explicitly tagging the underlying text corpus. this is achieved by assuming that the word pair consisting of the left and right neighbor of a particular token is characteristic of the part of speech at this position, and by clustering the neighbor pairs on the basis of their middle words as observed in a large corpus. the results obtained in this way are evaluated by comparing them to the part-of-speech distributions as found in the manually tagged brown corpus.
multilingual ontological analysis of european directives. this paper describes the main features of our tool called "legal taxonomy syllabus". the system is an ontology based tool designed to annotate and recover multi-lingua legal information and build conceptual dictionaries on european directives.
a translation aid system with a stratified lookup interface. we are currently developing a translation aid system specially designed for english-to-japanese volunteer translators working mainly online. in this paper we introduce the stratified reference lookup interface that has been incorporated into the source text area of the system, which distinguishes three user awareness levels depending on the type and nature of the reference unit. the different awareness levels are assigned to reference units from a variety of reference sources, according to the criteria of "composition", "difficulty", "speciality" and "resource type".
an api for measuring the relatedness of words in wikipedia. we present an api for computing the semantic relatedness of words in wikipedia.
a linguistic service ontology for language infrastructures. this paper introduces conceptual framework of an ontology for describing linguistic services on network-based language infrastructures. the ontology defines a taxonomy of processing resources and the associated static language resources. it also develops a sub-ontology for abstract linguistic objects such as expression, meaning, and description; these help define functionalities of a linguistic service. the proposed ontology is expected to serve as a solid basis for the interoperability of technical elements in language infrastructures.
an owl ontology for hpsg. the paper presents an owl ontology for hpsg. the hpsg ontology is integrated with an existing owl ontology, gold, as a community of practice extension. the basic ideas are illustrated by visualizations of type hierarchies for parts of speech.
measuring importance and query relevance in topic-focused multi-document summarization. the increasing complexity of summarization systems makes it difficult to analyze exactly which modules make a difference in performance. we carried out a principled comparison between the two most commonly used schemes for assigning importance to words in the context of query focused multi-document summarization: raw frequency (word probability) and log-likelihood ratio. we demonstrate that the advantages of log-likelihood ratio come from its known distributional properties which allow for the identification of a set of words that in its entirety defines the aboutness of the input. we also find that llr is more suitable for query-focused summarization since, unlike raw frequency, it is more sensitive to the integration of the information need defined by the user.
empirical measurements of lexical similarity in noun phrase conjuncts. the ability to detect similarity in conjunct heads is potentially a useful tool in helping to disambiguate coordination structures - a difficult task for parsers. we propose a distributional measure of similarity designed for such a task. we then compare several different measures of word similarity by testing whether they can empirically detect similarity in the head nouns of noun phrase conjuncts in the wall street journal (wsj) treebank. we demonstrate that several measures of word similarity can successfully detect conjunct head similarity and suggest that the measure proposed in this paper is the most appropriate for this task.
measuring syntactic difference in british english. recent work by nerbonne and wiersma (2006) has provided a foundation for measuring syntactic differences between corpora. it uses part-of-speech trigrams as an approximation to syntactic structure, comparing the trigrams of two corpora for statistically significant differences. this paper extends the method and its application. it extends the method by using leaf-path ancestors of sampson (2000) instead of trigrams, which capture internal syntactic structure---every leaf in a parse tree records the path back to the root. the corpus used for testing is the international corpus of english, great britain (nelson et al., 2002), which contains syntactically annotated speech of great britain. the speakers are grouped into geographical regions based on place of birth. this is different in both nature and number than previous experiments, which found differences between two groups of norwegian l2 learners of english. we show that dialectal variation in eleven british regions from the ice-gb is detectable by our algorithm, using both leaf-ancestor paths and trigrams.
exploiting structure for event discovery using the mdi algorithm. effectively identifying events in unstructured text is a very difficult task. this is largely due to the fact that an individual event can be expressed by several sentences. in this paper, we investigate the use of clustering methods for the task of grouping the text spans in a news article that refer to the same event. the key idea is to cluster the sentences, using a novel distance metric that exploits regularities in the sequential structure of events within a document. when this approach is compared to a simple bag of words baseline, a statistically significant increase in performance is observed.
shallow dependency labeling. we present a formalization of dependency labeling with integer linear programming. we focus on the integration of subcategorization into the decision making process, where the various subcategorization frames of a verb compete with each other. a maximum entropy model provides the weights for ilp optimization.
extracting hypernym pairs from the web. we apply pattern-based methods for collecting hypernym relations from the web. we compare our approach with hypernym extraction from morphological clues and from large text corpora. we show that the abundance of available data on the web enables obtaining good results with relatively unsophisticated techniques.
poliqarp: an open source corpus indexer and search engine with syntactic extensions. this paper presents recent extensions to poliqarp, an open source tool for indexing and searching morphosyntactically annotated corpora, which turn it into a tool for indexing and searching certain kinds of treebanks, complementary to existing treebank search engines. in particular, the paper discusses the motivation for such a new tool, the extended query syntax of poliqarp and implementation and efficiency issues.
semtag: a platform for specifying tree adjoining grammars and performing tag-based semantic construction. in this paper, we introduce semtag, a free and open software architecture for the development of tree adjoining grammars integrating a compositional semantics. semtag differs from xtag in two main ways. first, it provides an expressive grammar formalism and compiler for factorising and specifying tags. second, it supports semantic construction.
automatic prediction of cognate orthography using support vector machines. this paper describes an algorithm to automatically generate a list of cognates in a target language by means of support vector machines. while levenshtein distance was used to align the training file, no knowledge repository other than an initial list of cognates used for training purposes was input into the algorithm. evaluation was set up in a cognate production scenario which mimed a real-life situation where no word lists were available in the target language, delivering the ideal environment to test the feasibility of a more ambitious project that will involve language portability. an overall improvement of 50.58% over the baseline showed promising horizons.
extending marie: an n-gram-based smt decoder. in this paper we present several extensions of marie, a freely available n-gram-based statistical machine translation (smt) decoder. the extensions mainly consist of the ability to accept and generate word graphs and the introduction of two new n-gram models in the loglinear combination of feature functions the decoder implements. additionally, the decoder is enhanced with a caching strategy that reduces the number of n-gram calls improving the overall search efficiency. experiments are carried out over the eurpoean parliament spanish-english translation task.
an efficient algorithm for building a distributional thesaurus (and other sketch engine developments). gorman and curran (2006) argue that thesaurus generation for billion+-word corpora is problematic as the full computation takes many days. we present an algorithm with which the computation takes under two hours. we have created, and made publicly available, thesauruses based on large corpora for (at time of writing) seven major world languages. the development is implemented in the sketch engine (kilgarriff et al., 2004). another innovative development in the same tool is the presentation of the grammatical behaviour of a word against the background of how all other words of the same word class behave. thus, the english noun constraint occurs 75% in the plural. is this a salient lexical fact? to form a judgement, we need to know the distribution for all nouns. we use histograms to present the distribution in a way that is easy to grasp.