528 research outputs found

    Questionnaire integration system based on question classification and short text semantic textual similarity, A

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    2018 Fall.Includes bibliographical references.Semantic integration from heterogeneous sources involves a series of NLP tasks. Existing re- search has focused mainly on measuring two paired sentences. However, to find possible identical texts between two datasets, the sentences are not paired. To avoid pair-wise comparison, this thesis proposed a semantic similarity measuring system equipped with a precategorization module. It applies a hybrid question classification module, which subdivides all texts to coarse categories. The sentences are then paired from these subcategories. The core task is to detect identical texts between two sentences, which relates to the semantic textual similarity task in the NLP field. We built a short text semantic textual similarity measuring module. It combined conventional NLP techniques, including both semantic and syntactic features, with a Recurrent Convolutional Neural Network to accomplish an ensemble model. We also conducted a set of empirical evaluations. The results show that our system possesses a degree of generalization ability, and it performs well on heterogeneous sources

    Automatic grammar induction from free text using insights from cognitive grammar

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    Automatic identification of the grammatical structure of a sentence is useful in many Natural Language Processing (NLP) applications such as Document Summarisation, Question Answering systems and Machine Translation. With the availability of syntactic treebanks, supervised parsers have been developed successfully for many major languages. However, for low-resourced minority languages with fewer digital resources, this poses more of a challenge. Moreover, there are a number of syntactic annotation schemes motivated by different linguistic theories and formalisms which are sometimes language specific and they cannot always be adapted for developing syntactic parsers across different language families. This project aims to develop a linguistically motivated approach to the automatic induction of grammatical structures from raw sentences. Such an approach can be readily adapted to different languages including low-resourced minority languages. We draw the basic approach to linguistic analysis from usage-based, functional theories of grammar such as Cognitive Grammar, Computational Paninian Grammar and insights from psycholinguistic studies. Our approach identifies grammatical structure of a sentence by recognising domain-independent, general, cognitive patterns of conceptual organisation that occur in natural language. It also reflects some of the general psycholinguistic properties of parsing by humans - such as incrementality, connectedness and expectation. Our implementation has three components: Schema Definition, Schema Assembly and Schema Prediction. Schema Definition and Schema Assembly components were implemented algorithmically as a dictionary and rules. An Artificial Neural Network was trained for Schema Prediction. By using Parts of Speech tags to bootstrap the simplest case of token level schema definitions, a sentence is passed through all the three components incrementally until all the words are exhausted and the entire sentence is analysed as an instance of one final construction schema. The order in which all intermediate schemas are assembled to form the final schema can be viewed as the parse of the sentence. Parsers for English and Welsh (a low-resource minority language) were developed using the same approach with some changes to the Schema Definition component. We evaluated the parser performance by (a) Quantitative evaluation by comparing the parsed chunks against the constituents in a phrase structure tree (b) Manual evaluation by listing the range of linguistic constructions covered by the parser and by performing error analysis on the parser outputs (c) Evaluation by identifying the number of edits required for a correct assembly (d) Qualitative evaluation based on Likert scales in online surveys

    Statistical Parsing by Machine Learning from a Classical Arabic Treebank

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    Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year

    Predicting speculation: a simple disambiguation approach to hedge detection in biomedical literature

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    <p>Abstract</p> <p>Background</p> <p>This paper presents a novel approach to the problem of <it>hedge detection</it>, which involves identifying so-called hedge cues for labeling sentences as certain or uncertain. This is the classification problem for Task 1 of the CoNLL-2010 Shared Task, which focuses on hedging in the biomedical domain. We here propose to view hedge detection as a simple disambiguation problem, restricted to words that have previously been observed as hedge cues. As the feature space for the classifier is still very large, we also perform experiments with dimensionality reduction using the method of <it>random indexing</it>.</p> <p>Results</p> <p>The SVM-based classifiers developed in this paper achieves the best published results so far for sentence-level uncertainty prediction on the CoNLL-2010 Shared Task test data. We also show that the technique of random indexing can be successfully applied for reducing the dimensionality of the original feature space by several orders of magnitude, without sacrificing classifier performance.</p> <p>Conclusions</p> <p>This paper introduces a simplified approach to detecting speculation or uncertainty in text, focusing on the biomedical domain. Evaluated at the sentence-level, our SVM-based classifiers achieve the best published results so far. We also show that the feature space can be aggressively compressed using random indexing while still maintaining comparable classifier performance.</p
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