waashk/dblp huggingface dataset:
word-sense disambiguation for machine translation. in word sense disambiguation, a system attempts to determine the sense of a word from contextual features. major barriers to building a high-performing word sense disambiguation system include the difficulty of labeling data for this task and of predicting fine-grained sense distinctions. these issues stem partly from the fact that the task is being treated in isolation from possible uses of automatically disambiguated data. in this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. we can use parallel language corpora as a large supply of partially labeled data for this task. we present algorithms for solving the word translation problem and demonstrate a significant improvement over a baseline system. we then show that the word-translation system can be used to improve performance on a simplified machine-translation task and can effectively and accurately prune the set of candidate translations for a word.
speech-based information retrieval system with clarification dialogue strategy. this paper addresses a dialogue strategy to clarify and constrain the queries for speech-driven document retrieval systems. in spoken dialogue interfaces, users often make utterances before the query is completely generated in their mind; thus input queries are often vague or fragmental. as a result, usually many items are matched. we propose an efficient dialogue framework, where the system dynamically selects an optimal question based on information gain (ig), which represents reduction of matched items. a set of possible questions is prepared using various knowledge sources. as a bottom-up knowledge source, we extract a list of words that can take a number of objects and potentially causes ambiguity, using a dependency structure analysis of the document texts. this is complemented by top-down knowledge sources of metadata and hand-crafted questions. an experimental evaluation showed that the method significantly improved the success rate of retrieval, and all categories of the prepared questions contributed to the improvement.
a shortest path dependency kernel for relation extraction. we present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. experiments on extracting top-level relations from the ace (automated content extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
a semantic scattering model for the automatic interpretation of genitives. this paper addresses the automatic classification of the semantic relations expressed by the english genitives. a learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations on a large corpus. the semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. the algorithm was tested on a corpus of approximately 2,000 sentences and achieved an accuracy of 79%, far better than 44% accuracy obtained with c5.0, or 43% obtained with a naive bayes algorithm, or 27% accuracy with a support vector machines learner on the same corpus.
mboi: discovery of business opportunities on the internet. we propose a tool for the discovery of business opportunities on the web, more specifically to help a user find relevant call for tenders (cft), i.e. invitations to contractors to submit a tender for their products/services. simple keyword-based information retrieval do not capture the relationships in the data, which are needed to answer the complex needs of the users. we therefore augment keywords with information extracted through natural language processing and business intelligence tools. as opposed to most systems, this information is used at all stages in the back-end and interface. the benefits are twofold: first we obtain higher precision of search and classification, and second the user gains access to a deeper level of information.
a backoff model for bootstrapping resources for non-english languages. the lack of annotated data is an obstacle to the development of many natural language processing applications; the problem is especially severe when the data is non-english. previous studies suggested the possibility of acquiring resources for non-english languages by bootstrapping from high quality english nlp tools and parallel corpora; however, the success of these approaches seems limited for dissimilar language pairs. in this paper, we propose a novel approach of combining a bootstrapped resource with a small amount of manually annotated data. we compare the proposed approach with other bootstrapping methods in the context of training a chinese part-of-speech tagger. experimental results show that our proposed approach achieves a significant improvement over em and self-training and systems that are only trained on manual annotations.
composition of conditional random fields for transfer learning. many learning tasks have subtasks for which much training data exists. therefore, we want to transfer learning from the old, general-purpose subtask to a more specific new task, for which there is often less data. while work in transfer learning often considers how the old task should affect learning on the new task, in this paper we show that it helps to take into account how the new task affects the old. specifically, we perform joint decoding of separately-trained sequence models, preserving uncertainty between the tasks and allowing information from the new task to affect predictions on the old task. on two standard text data sets, we show that joint decoding outperforms cascaded decoding.
enhanced answer type inference from questions using sequential models. question classification is an important step in factual question answering (qa) and other dialog systems. several attempts have been made to apply statistical machine learning approaches, including support vector machines (svms) with sophisticated features and kernels. curiously, the payoff beyond a simple bag-of-words representation has been small. we show that most questions reveal their class through a short contiguous token subsequence, which we call its informer span. perfect knowledge of informer spans can enhance accuracy from 79.4% to 88% using linear svms on standard benchmarks. in contrast, standard heuristics based on shallow pattern-matching give only a 3% improvement, showing that the notion of an informer is non-trivial. using a novel multi-resolution encoding of the question's parse tree, we induce a conditional random field (crf) to identify informer spans with about 85% accuracy. then we build a meta-classifier using a linear svm on the crf output, enhancing accuracy to 86.2%, which is better than all published numbers.
using mona for querying linguistic treebanks. mona is an automata toolkit providing a compiler for compiling formulae of monadic second order logic on strings or trees into string automata or tree automata. in this paper, we evaluate the option of using mona as a treebank query tool. unfortunately, we find that mona is not an option. there are several reasons why the main being unsustainable query answer times. if the treebank contains larger trees with more than 100 nodes, then even the processing of simple queries may take hours.
parallelism in coordination as an instance of syntactic priming: evidence from corpus-based modeling. experimental research in psycholinguistics has demonstrated a parallelism effect in coordination: speakers are faster at processing the second conjunct of a coordinate structure if it has the same internal structure as the first conjunct. we show that this phenomenon can be explained by the prevalence of parallel structures in corpus data. we demonstrate that parallelism is not limited to coordination, but also applies to arbitrary syntactic configurations, and even to documents. this indicates that the parallelism effect is an instance of a general syntactic priming mechanism in human language processing.
translating with non-contiguous phrases. this paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. a method for producing such phrases from a word-aligned corpora is proposed. a statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the nist evaluation metric. translations are produced by means of a beam-search decoder. experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data.
inner-outer bracket models for word alignment using hidden blocks. most statistical translation systems are based on phrase translation pairs, or "blocks", which are obtained mainly from word alignment. we use blocks to infer better word alignment and improved word alignment which, in turn, leads to better inference of blocks. we propose two new probabilistic models based on the inner-outer segmentations and use em algorithms for estimating the models' parameters. the first model recovers ibm model-1 as a special case. both models outperform bidirectional ibm model-4 in terms of word alignment accuracy by 10% absolute on the f-measure. using blocks obtained from the models in actual translation systems yields statistically significant improvements in chinese-english smt evaluation.
local phrase reordering models for statistical machine translation. we describe stochastic models of local phrase movement that can be incorporated into a statistical machine translation (smt) system. these models provide properly formulated, non-deficient, probability distributions over reordered phrase sequences. they are implemented by weighted finite state transducers. we describe em-style parameter re-estimation procedures based on phrase alignment under the complete translation model incorporating reordering. our experiments show that the reordering model yields substantial improvements in translation performance on arabic-to-english and chinese-to-english mt tasks. we also show that the procedure scales as the bitext size is increased.
accurate function parsing. in this paper, we extend an existing parser to produce richer output annotated with function labels. we obtain state-of-the-art results both in function labelling and in parsing, by automatically relabelling the penn treebank trees. in particular, we obtain the best published results on semantic function labels. this suggests that current statistical parsing methods are sufficiently general to produce accurate shallow semantic annotation.
query expansion with the minimum user feedback by transductive learning. query expansion techniques generally select new query terms from a set of top ranked documents. although a user's manual judgment of those documents would much help to select good expansion terms, it is difficult to get enough feedback from users in practical situations. in this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document. in order to tackle this specific condition, we introduce two refinements to a well-known query expansion technique. one is application of a transductive learning technique in order to increase relevant documents. the other is a modified parameter estimation method which laps the predictions by multiple learning trials and try to differentiate the importance of candidate terms for expansion in relevant documents. experimental results show that our technique outperforms some traditional query expansion methods in several evaluation measures.
extracting personal names from email: applying named entity recognition to informal text. there has been little prior work on named entity recognition for "informal" documents like email. we present two methods for improving performance of person name recognizers for email: email-specific structural features and a recall-enhancing method which exploits name repetition across multiple documents.
detection of entity mentions occuring in english and chinese text. in this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language independent system. it is optimal in the sense that all categorical constraints are simultaneously considered. the system is compact and easy to develop and maintain, since only a single set of features and classifiers are needed to be designed and optimized. it is implemented using one-versus-all support vector machine (svm) classifiers and a number of feature extractors at several linguistic levels. svms are well known for their ability to handle a large set of overlapping features with theoretically sound generalization properties. data sparsity might be an important issue as a result of a large number of classes and relatively moderate training data size. however, we report results that the integrated system performs as good as a pipelined system that decomposes the problem into a few smaller sub-tasks. we conduct all our experiments using ace 2004 data, evaluate the systems using ace metrics and report competitive performance.
using random walks for question-focused sentence retrieval. we consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. annotators generated a list of questions central to understanding each story in our corpus. because of the dynamic nature of the stories, many questions are time-sensitive (e.g. "how many victims have been found?") judges found sentences providing an answer to each question. to address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to the input question via idf-weighted word overlap. in our experiments, the method achieves a trdr score that is significantly higher than that of the baseline.
combining deep linguistics analysis and surface pattern learning: a hybrid approach to chinese definitional question answering. we explore a hybrid approach for chinese definitional question answering by combining deep linguistic analysis with surface pattern learning. we answer four questions in this study: 1) how helpful are linguistic analysis and pattern learning? 2) what kind of questions can be answered by pattern matching? 3) how much annotation is required for a pattern-based system to achieve good performance? 4) what linguistic features are most useful? extensive experiments are conducted on biographical questions and other definitional questions. major findings include: 1) linguistic analysis and pattern learning are complementary; both are required to make a good definitional qa system; 2) pattern matching is very effective in answering biographical questions while less effective for other definitional questions; 3) only a small amount of annotation is required for a pattern learning system to achieve good performance on biographical questions; 4) the most useful linguistic features are copulas and appositives; relations also play an important role; only some propositions convey vital facts.
inducing a multilingual dictionary from a parallel multitext in related languages. dictionaries and word translation models are used by a variety of systems, especially in machine translation. we build a multilingual dictionary induction system for a family of related resource-poor languages. we assume only the presence of a single medium-length multitext (the bible). the techniques rely upon lexical and syntactic similarity of languages as well as on the fact that building dictionaries for several pairs of languages provides information about other pairs.
the mit spoken lecture processing project. we will demonstrate the mit spoken lecture processing server and an accompanying lecture browser that students can use to quickly locate and browse lecture segments that apply to their query. we will show how lecturers can upload recorded lectures and companion text material to our server for automatic processing. the server automatically generates a time-aligned word transcript of the lecture which can be downloaded for use within a browser. we will also demonstrate a browser we have created which allows students to quickly locate and browse audio segments that are relevant to their query. these tools can provide students with easier access to audio (or audio/visual) lectures, hopefully improving their educational experience.
incremental ltag parsing. we present a very efficient statistical incremental parser for ltag-spinal, a variant of ltag. the parser supports the full adjoining operation, dynamic predicate coordination, and non-projective dependencies, with a formalism of provably stronger generative capacity as compared to cfg. using gold standard pos tags as input, on section 23 of the ptb, the parser achieves an f-score of 89.3% for syntactic dependency defined on ltag derivation trees, which are deeper than the dependencies extracted from ptb alone with head rules (for example, in magerman's style).
the use of metadata, web-derived answer patterns and passage context to improve reading comprehension performance. a reading comprehension (rc) system attempts to understand a document and returns an answer sentence when posed with a question. rc resembles the ad hoc question answering (qa) task that aims to extract an answer from a collection of documents when posed with a question. however, since rc focuses only on a single document, the system needs to draw upon external knowledge sources to achieve deep analysis of passage sentences for answer sentence extraction. this paper proposes an approach towards rc that attempts to utilize external knowledge to improve performance beyond the baseline set by the bag-of-words (bow) approach. our approach emphasizes matching of metadata (i.e. verbs, named entities and base noun phrases) in passage context utilization and answer sentence extraction. we have also devised an automatic acquisition process for web-derived answer patterns (ap) which utilizes question-answer pairs from trec qa, the google search engine and the web. this approach gave improved rc performances for both the remedia and chunghwa corpora, attaining humsent accuracies of 42% and 69% respectively. in particular, performance analysis based on remedia shows that relative performances of 20.7% is due to metadata matching and a further 10.9% is due to the application of web-derived answer patterns.
using the web as an implicit training set: application to structural ambiguity resolution. recent work has shown that very large corpora can act as training data for nlp algorithms even without explicit labels. in this paper we show how the use of surface features and paraphrases in queries against search engines can be used to infer labels for structural ambiguity resolution tasks. using unsupervised algorithms, we achieve 84% precision on pp-attachment and 80% on noun compound coordination.
automatically learning cognitive status for multi-document summarization of newswire. machine summaries can be improved by using knowledge about the cognitive status of news article referents. in this paper, we present an approach to automatically acquiring distinctions in cognitive status using machine learning over the forms of referring expressions appearing in the input. we focus on modeling references to people, both because news often revolve around people and because existing natural language tools for named entity identification are reliable. we examine two specific distinctions---whether a person in the news can be assumed to be known to a target audience (hearer-old vs hearer-new) and whether a person is a major character in the news story. we report on machine learning experiments that show that these distinctions can be learned with high accuracy, and validate our approach using human subjects.
a maximum entropy word aligner for arabic-english machine translation. this paper presents a maximum entropy word alignment algorithm for arabic-english based on supervised training data. we demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance. the probabilistic model used in the alignment directly models the link decisions. significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests. performance of the algorithm is contrasted with human annotation performance.
extracting product features and opinions from reviews. consumers are often forced to wade through many on-line reviews in order to make an informed product choice. this paper introduces opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.compared to previous work, opine achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.
opine: extracting product features and opinions from reviews. consumers have to often wade through a large number of on-line reviews in order to make an informed product choice. we introduce opine, an unsupervised, high-precision information extraction system which mines product reviews in order to build a model of product features and their evaluation by reviewers.
webexperimenter for multiple-choice question generation. automatic generation of multiple-choice questions is an emerging topic in application of natural language processing. particularly, applying it to language testing has been proved to be useful (sumita et al., 2005).
a self-learning context-aware lemmatizer for german. accurate lemmatization of german nouns mandates the use of a lexicon. comprehensive lexicons, however, are expensive to build and maintain. we present a self-learning lemmatizer capable of automatically creating a full-form lexicon by processing german documents.
data-driven approaches for information structure identification. this paper investigates automatic identification of information structure (is) in texts. the experiments use the prague dependency treebank which is annotated with is following the praguian approach of topic focus articulation. we automatically detect t(opic) and f(ocus), using node attributes from the treebank as basic features and derived features inspired by the annotation guidelines. we present the performance of decision trees (c4.5), maximum entropy, and rule induction (ripper) classifiers on all tectogrammatical nodes. we compare the results against a baseline system that always assigns f(ocus) and against a rule-based system. the best system achieves an accuracy of 90.69%, which is a 44.73% improvement over the baseline (62.66%).