waashk/dblp huggingface dataset:
position specific posterior lattices for indexing speech. the paper presents the position specific posterior lattice, a novel representation of automatic speech recognition lattices that naturally lends itself to efficient indexing of position information and subsequent relevance ranking of spoken documents using proximity.in experiments performed on a collection of lecture recordings --- mit icampus data --- the spoken document ranking accuracy was improved by 20% relative over the commonly used baseline of indexing the 1-best output from an automatic speech recognizer. the mean average precision (map) increased from 0.53 when using 1-best output to 0.62 when using the new lattice representation. the reference used for evaluation is the output of a standard retrieval engine working on the manual transcription of the speech collection.albeit lossy, the pspl lattice is also much more compact than the asr 3-gram lattice from which it is computed --- which translates in reduced inverted index size as well --- at virtually no degradation in word-error-rate performance. since new paths are introduced in the lattice, the oracle accuracy increases over the original asr lattice.
speech ogle: indexing uncertainty for spoken document search. the paper presents the position specific posterior lattice (pspl), a novel lossy representation of automatic speech recognition lattices that naturally lends itself to efficient indexing and subsequent relevance ranking of spoken documents.in experiments performed on a collection of lecture recordings --- mit icampus data --- the spoken document ranking accuracy was improved by 20% relative over the commonly used baseline of indexing the 1-best output from an automatic speech recognizer.the inverted index built from pspl lattices is compact --- about 20% of the size of 3-gram asr lattices and 3% of the size of the uncompressed speech --- and it allows for extremely fast retrieval. furthermore, little degradation in performance is observed when pruning pspl lattices, resulting in even smaller indexes --- 5% of the size of 3-gram asr lattices.
exploiting syntactic structure for language modeling. the paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. the model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation and operates in a left-to-right manner --- therefore usable for automatic speech recognition. the model, its probabilistic parameterization, and a set of experiments meant to evaluate its predictive power are presented; an improvement over standard trigram modeling is achieved.
aligning sentences in bilingual corpora using lexical information. in this paper, we describe a fast algorithm for aligning sentences with their translations in a bilingual corpus. existing efficient algorithms ignore word identities and only consider sentence length (brown et al., 1991b; gale and church, 1991). our algorithm constructs a simple statistical word-to-word translation model on the fly during alignment. we find the alignment that maximizes the probability of generating the corpus with this translation model. we have achieved an error rate of approximately 0.4% on canadian hansard data, which is a significant improvement over previous results. the algorithm is language independent.
bayesian grammar induction for language modeling. we describe a corpus-based induction algorithm for probabilistic context-free grammars. the algorithm employs a greedy heuristic search within a bayesian framework, and a post-pass using the inside-outside algorithm. we compare the performance of our algorithm to n-gram models and the inside-outside algorithm in three language modeling tasks. in two of the tasks, the training data is generated by a probabilistic context-free grammar and in both tasks our algorithm outperforms the other techniques. the third task involves naturally-occurring data, and in this task our algorithm does not perform as well as n-gram models but vastly outperforms the inside-outside algorithm.
resolving translation ambiguity and target polysemy in cross-language information retrieval. this paper deals with translation ambiguity and target polysemy problems together. two monolingual balanced corpora are employed to learn word co-occurrence for translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution. experiments show that the model achieves 62.92% of monolingual information retrieval, and is 40.80% addition to the select-all model. combining the target polysemy resolution, the retrieval performance is about 10.11% increase to the model resolving translation ambiguity only.
a high-accurate chinese-english ne backward translation system combining both lexical information and web statistics. named entity translation is indispensable in cross language information retrieval nowadays. we propose an approach of combining lexical information, web statistics, and inverse search based on google to backward translate a chinese named entity (ne) into english. our system achieves a high top-1 accuracy of 87.6%, which is a relatively good performance reported in this area until present.
extracting noun phrases from large-scale texts: a hybrid approach and its automatic evaluation. to acquire noun phrases from running texts is useful for many applications, such as word grouping, terminology indexing, etc. the reported literatures adopt pure probabilistic approach, or pure rule-based noun phrases grammar to tackle this problem. in this paper, we apply a probabilistic chunker to deciding the implicit boundaries of constituents and utilize the linguistic knowledge to extract the noun phrases by a finite state mechanism. the test texts are susanne corpus and the results are evaluated by comparing the parse field of susanne corpus automatically. the results of this preliminary experiment are encouraging.
a concept-based adaptive approach to word sense disambiguation. word sense disambiguation for unrestricted text is one of the most difficult tasks in the fields of computational linguistics. the crux of the problem is to discover a model that relates the intended sense of a word with its context. this paper describes a general framework for adaptive conceptual word sense disambiguation. central to this wsd framework is the sense division and semantic relations based on topical analysis of dictionary sense definitions. the process begins with an initial disambiguation step using an mrd-derived knowledge base. an adaptation step follows to combine the initial knowledge base with knowledge gleaned from the partial disambiguated text. once the knowledge base is adjusted to suit the text at hand, it is then applied to the text again to finalize the disambiguation result. definitions and example sentences from ldoce are employed as training materials for wsd, while passages from the brown corpus and wall street journal are used for testing. we report on several experiments illustrating effectiveness of the adaptive approach.
an empirical study of smoothing techniques for language modeling. we present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by jelinek and mercer (1980), katz (1987), and church and gale (1991). we investigate for the first time how factors such as training data size, corpus (e.g., brown versus wall street journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. in addition, we introduce two novel smoothing techniques, one a variation of jelinek-mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
proper name translation in cross-language information retrieval. recently, language barrier becomes the major problem for people to search, retrieve, and understand www documents in different languages. this paper deals with query translation issue in cross-language information retrieval, proper names in particular. models for name identification, name translation and name searching are presented. the recall rates and the precision rates for the identification of chinese organization names, person names and location names under met data are (76.67%, 79.33%), (87.33%, 82.33%) and (77.00%, 82.00%), respectively. in name translation, only 0.79% and 1.11% of candidates for english person names and location names, respectively, have to be proposed. the name searching facility is implemented on an mt sever for information retrieval on the www. under this system, user can issue queries and read documents with his familiar language.
relation extraction using label propagation based semi-supervised learning. shortage of manually labeled data is an obstacle to supervised relation extraction methods. in this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (lp) algorithm, for relation extraction. it represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. experiment results on the ace corpus showed that this lp algorithm achieves better performance than svm when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task.
unsupervised relation disambiguation using spectral clustering. this paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. it works by calculating eigen-vectors of an adjacency graph's laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. experiment results on ace corpora show that this spectral clustering based approach outperforms the other clustering methods.
a new statistical approach to chinese pinyin input. chinese input is one of the key challenges for chinese pc users. this paper proposes a statistical approach to pinyin-based chinese input. this approach uses a trigram-based language model and a statistically based segmentation. also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based pinyin input, and a spelling model for english which enables modeless pinyin input.
fast - an automatic generation system for grammar tests. this paper introduces a method for the semi-automatic generation of grammar test items by applying natural language processing (nlp) techniques. based on manually-designed patterns, sentences gathered from the web are transformed into tests on grammaticality. the method involves representing test writing knowledge as test patterns, acquiring authentic sentences on the web, and applying generation strategies to transform sentences into items. at runtime, sentences are converted into two types of toefl-style question: multiple-choice and error detection. we also describe a prototype system fast (free assessment of structural tests). evaluation on a set of generated questions indicates that the proposed method performs satisfactory quality. our methodology provides a promising approach and offers significant potential for computer assisted language learning and assessment.
novel association measures using web search with double checking. a web search with double checking model is proposed to explore the web as a live corpus. five association measures including variants of dice, overlap ratio, jaccard, and cosine, as well as co-occurrence double check (codc), are presented. in the experiments on rubenstein-goodenough's benchmark data set, the codc measure achieves correlation coefficient 0.8492, which competes with the performance (0.8914) of the model using wordnet. the experiments on link detection of named entities using the strategies of direct association, association matrix and scalar association matrix verify that the double-check frequencies are reliable. further study on named entity clustering shows that the five measures are quite useful. in particular, codc measure is very stable on word-word and name-name experiments. the application of codc measure to expand community chains for personal name disambiguation achieves 9.65% and 14.22% increase compared to the system without community expansion. all the experiments illustrate that the novel model of web search with double checking is feasible for mining associations from the web.
chinese verb sense discrimination using an em clustering model with rich linguistic features. this paper discusses the application of the expectation-maximization (em) clustering algorithm to the task of chinese verb sense discrimination. the model utilized rich linguistic features that capture predicate-argument structure information of the target verbs. a semantic taxonomy for chinese nouns, which was built semi-automatically based on two electronic chinese semantic dictionaries, was used to provide semantic features for the model. purity and normalized mutual information were used to evaluate the clustering performance on 12 chinese verbs. the experimental results show that the em clustering model can learn sense or sense group distinctions for most of the verbs successfully. we further enhanced the model with certain fine-grained semantic categories called lexical sets. our results indicate that these lexical sets improve the model's performance for the three most challenging verbs chosen from the first set of experiments.
pat-trees with the deletion function as the learning device for linguistic patterns. in this study, a learning device based on the pat-tree data structures was developed. the original pat-trees were enhanced with the deletion function to emulate human learning competence. the learning process worked as follows. the linguistic patterns from the text corpus are inserted into the pat-tree one by one. since the memory was limited, hopefully, the important and new patterns would be retained in the pat-tree and the old and unimportant patterns would be released from the tree automatically. the proposed pat-trees with the deletion function have the following advantages. 1) they are easy to construct and maintain. 2) any prefix substring and its frequency count through pat-tree can be searched very quickly. 3) the space requirement for a pat-tree is linear with respect to the size of the input text. 4) the insertion of a new element can be carried out at any time without being blocked by the memory constraints because the free space is released through the deletion of unimportant elements.experiments on learning high frequency bigrams were carried out under different memory size constraints. high recall rates were achieved. the results show that the proposed pat-trees can be used as on-line learning devices.
an empirical study of chinese chunking. in this paper, we describe an empirical study of chinese chunking on a corpus, which is extracted from upenn chinese treebank-4 (ctb4). first, we compare the performance of the state-of-the-art machine learning models. then we propose two approaches in order to improve the performance of chinese chunking. 1) we propose an approach to resolve the special problems of chinese chunking. this approach extends the chunk tags for every problem by a tag-extension function. 2) we propose two novel voting methods based on the characteristics of chunking task. compared with traditional voting methods, the proposed voting methods consider long distance information. the experimental results show that the svms model outperforms the other models and that our proposed approaches can improve performance significantly.
reranking answers for definitional qa using language modeling. statistical ranking methods based on centroid vector (profile) extracted from external knowledge have become widely adopted in the top definitional qa systems in trec 2003 and 2004. in these approaches, terms in the centroid vector are treated as a bag of words based on the independent assumption. to relax this assumption, this paper proposes a novel language model-based answer reranking method to improve the existing bag-of-words model approach by considering the dependence of the words in the centroid vector. experiments have been conducted to evaluate the different dependence models. the results on the trec 2003 test set show that the reranking approach with biterm language model, significantly outperforms the one with the bag-of-words model and unigram language model by 14.9% and 12.5% respectively in f-measure(5).
embedding new information into referring expressions. this paper focuses on generating referring expressions capable of serving multiple communicative goals. the components of a referring expression are divided into a referring part and a non-referring part. two rules for the content determination and construction of the non-referring part are given, which are realised in an embedding algorithm. the significant aspect of our approach is that it intends to generate the non-referring part given the restrictions imposed by the referring part, whose realisation is, on the other hand, affected by the non-referring part.
a probability model to improve word alignment. word alignment plays a crucial role in statistical machine translation. word-aligned corpora have been found to be an excellent source of translation-related knowledge. we present a statistical model for computing the probability of an alignment given a sentence pair. this model allows easy integration of context-specific features. our experiments show that this model can be an effective tool for improving an existing word alignment.
soft syntactic constraints for word alignment through discriminative training. word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. however, this hard constraint can also rule out correct alignments, and its utility decreases as alignment models become more complex. we use a publicly available structured output svm to create a max-margin syntactic aligner with a soft cohesion constraint. the resulting aligner is the first, to our knowledge, to use a discriminative learning method to train an itg bitext parser.
statistical parsing with an automatically-extracted tree adjoining grammar. we discuss the advantages of lexicalized tree-adjoining grammar as an alternative to lexicalized pcfg for statistical parsing, describing the induction of a probabilistic ltag model from the penn treebank and evaluating its parsing performance. we find that this induction method is an improvement over the em-based method of (hwa, 1998), and that the induced model yields results comparable to lexicalized pcfg.
constraints on strong generative power. we consider the question "how much strong generative power can be squeezed out of a formal system without increasing its weak generative power?" and propose some theoretical and practical constraints on this problem. we then introduce a formalism which, under these constraints, maximally squeezes strong generative power out of context-free grammar. finally, we generalize this result to formalisms beyond cfg.
a hierarchical phrase-based model for statistical machine translation. we present a statistical phrase-based translation model that uses hierarchical phrases---phrases that contain subphrases. the model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. thus it can be seen as a shift to the formal machinery of syntax-based translation systems without any linguistic commitment. in our experiments using bleu as a metric, the hierarchical phrase-based model achieves a relative improvement of 7.5% over pharaoh, a state-of-the-art phrase-based system.
a preference-first language processor integrating the unification grammar and markov language model for speech recognition applications. a language processor is to find out a most promising sentence hypothesis for a given word lattice obtained from acoustic signal recognition. in this paper a new language processor is proposed, in which unification grammar and markov language model are integrated in a word lattice parsing algorithm based on an augmented chart, and the island-driven parsing concept is combined with various preference-first parsing strategies defined by different construction principles and decision rules. test results show that significant improvements in both correct rate of recognition and computation speed can be achieved.
teaching a weaker classifier: named entity recognition on upper case text. this paper describes how a machine-learning named entity recognizer (ner) on upper case text can be improved by using a mixed case ner and some unlabeled text. the mixed case ner can be used to tag some unlabeled mixed case text, which are then used as additional training material for the upper case ner. we show that this approach reduces the performance gap between the mixed case ner and the upper case ner substantially, by 39% for muc-6 and 22% for muc-7 named entity test data. our method is thus useful in improving the accuracy of ners on upper case text, such as transcribed text from automatic speech recognizers where case information is missing.
closing the gap: learning-based information extraction rivaling knowledge-engineering methods. in this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. when tested on the muc-4 task, our learning approach achieves accuracy competitive to the best of the muc-4 systems, which were all built with manually engineered rules. our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. to our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge engineering approach.
an account for compound prepositions in farsi. there are some sorts of 'preposition + noun' combinations in farsi that apparently a prepositional phrase almost behaves as compound prepositions. as they are not completely behaving as compounds, it is doubtful that the process of word formation is a morphological one. the analysis put forward by this paper proposes "incorporation" by which an n&deg; is incorporated to a p&deg; constructing a compound preposition. in this way tagging prepositions and parsing texts in natural language processing is defined in a proper manner.
extracting semantic hierarchies from a large on-line dictionary. dictionaries are rich sources of detailed semantic information, but in order to use the information for natural language processing, it must be organized systematically. this paper describes automatic and semi-automatic procedures for extracting and organizing semantic feature information implicit in dictionary definitions. two head-finding heuristics are described for locating the genus terms in noun and verb definitions. the assumption is that the genus term represents inherent features of the word it defines. the two heuristics have been used to process definitions of 40,000 nouns and 8,000 verbs, producing indexes in which each genus term is associated with the words it defined. the sprout program interactively grows a taxonomic "tree" from any specified root feature by consulting the genus index. its output is a tree in which all of the nodes have the root feature for at least one of their senses. the filter program uses an inverted form of the genus index. filtering begins with an initial filter file consisting of words that have a given feature (e.g. [+human]) in all of their senses. the program then locates, in the index, words whose genus terms all appear in the filter file. the output is a list of new words that have the given feature in all of their senses.