Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Automatic Segmentation and Part-Of-Speech Tagging For Tibetan: A First Step Towards Machine Translation

This paper presents what we believe to be the first reported work on Tibetan machine translation (MT). Of the three conceptually distinct components of a MT system — analysis, transfer, and generation — the first phase, consisting of POS tagging has been successfully completed. The combination POS tagger / word-segmenter was manually constructed as a rule-based multi-tagger relying on the Wilson formulation of Tibetan grammar. Partial parsing was also performed in combination with POS-tag sequence disambiguation. The component was evaluated at the task of document indexing for Information Retrieval (IR). Preliminary analysis indicated slightly better (though statistically comparable) performance to n-gram based approaches at a known-item IR task. Although segmentation is application specific, error analysis placed segmentation accuracy at 99%; the accuracy of the POS tagger is also estimated at 99% based on IR error analysis and random sampling

The text classification pipeline: Starting shallow, going deeper

An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC.An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC

Automatic Evaluation and Uniform Filter Cascades for Inducing N-Best Translation Lexicons

This paper shows how to induce an N-best translation lexicon from a bilingual text corpus using statistical properties of the corpus together with four external knowledge sources. The knowledge sources are cast as filters, so that any subset of them can be cascaded in a uniform framework. A new objective evaluation measure is used to compare the quality of lexicons induced with different filter cascades. The best filter cascades improve lexicon quality by up to 137% over the plain vanilla statistical method, and approach human performance. Drastically reducing the size of the training corpus has a much smaller impact on lexicon quality when these knowledge sources are used. This makes it practical to train on small hand-built corpora for language pairs where large bilingual corpora are unavailable. Moreover, three of the four filters prove useful even when used with large training corpora.Comment: To appear in Proceedings of the Third Workshop on Very Large Corpora, 15 pages, uuencoded compressed PostScrip

Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models

This dissertation focuses on effective combination of data-driven natural language processing (NLP) approaches with linguistic knowledge sources that are based on manual text annotation or word grouping according to semantic commonalities. I gainfully apply fine-grained linguistic soft constraints -- of syntactic or semantic nature -- on statistical NLP models, evaluated in end-to-end state-of-the-art statistical machine translation (SMT) systems. The introduction of semantic soft constraints involves intrinsic evaluation on word-pair similarity ranking tasks, extension from words to phrases, application in a novel distributional paraphrase generation technique, and an introduction of a generalized framework of which these soft semantic and syntactic constraints can be viewed as instances, and in which they can be potentially combined. Fine granularity is key in the successful combination of these soft constraints, in many cases. I show how to softly constrain SMT models by adding fine-grained weighted features, each preferring translation of only a specific syntactic constituent. Previous attempts using coarse-grained features yielded negative results. I also show how to softly constrain corpus-based semantic models of words (“distributional profiles”) to effectively create word-sense-aware models, by using semantic word grouping information found in a manually compiled thesaurus. Previous attempts, using hard constraints and resulting in aggregated, coarse-grained models, yielded lower gains. A novel paraphrase generation technique incorporating these soft semantic constraints is then also evaluated in a SMT system. This paraphrasing technique is based on the Distributional Hypothesis. The main advantage of this novel technique over current “pivoting” techniques for paraphrasing is the independence from parallel texts, which are a limited resource. The evaluation is done by augmenting translation models with paraphrase-based translation rules, where fine-grained scoring of paraphrase-based rules yields significantly higher gains. The model augmentation includes a novel semantic reinforcement component: In many cases there are alternative paths of generating a paraphrase-based translation rule. Each of these paths reinforces a dedicated score for the “goodness” of the new translation rule. This augmented score is then used as a soft constraint, in a weighted log-linear feature, letting the translation model learn how much to “trust” the paraphrase-based translation rules. The work reported here is the first to use distributional semantic similarity measures to improve performance of an end-to-end phrase-based SMT system. The unified framework for statistical NLP models with soft linguistic constraints enables, in principle, the combination of both semantic and syntactic constraints -- and potentially other constraints, too -- in a single SMT model

D6.2 Integrated Final Version of the Components for Lexical Acquisition

The PANACEA project has addressed one of the most critical bottlenecks that threaten the development of technologies to support multilingualism in Europe, and to process the huge quantity of multilingual data produced annually. Any attempt at automated language processing, particularly Machine Translation (MT), depends on the availability of language-specific resources. Such Language Resources (LR) contain information about the language\u27s lexicon, i.e. the words of the language and the characteristics of their use. In Natural Language Processing (NLP), LRs contribute information about the syntactic and semantic behaviour of words - i.e. their grammar and their meaning - which inform downstream applications such as MT. To date, many LRs have been generated by hand, requiring significant manual labour from linguistic experts. However, proceeding manually, it is impossible to supply LRs for every possible pair of European languages, textual domain, and genre, which are needed by MT developers. Moreover, an LR for a given language can never be considered complete nor final because of the characteristics of natural language, which continually undergoes changes, especially spurred on by the emergence of new knowledge domains and new technologies. PANACEA has addressed this challenge by building a factory of LRs that progressively automates the stages involved in the acquisition, production, updating and maintenance of LRs required by MT systems. The existence of such a factory will significantly cut down the cost, time and human effort required to build LRs. WP6 has addressed the lexical acquisition component of the LR factory, that is, the techniques for automated extraction of key lexical information from texts, and the automatic collation of lexical information into LRs in a standardized format. The goal of WP6 has been to take existing techniques capable of acquiring syntactic and semantic information from corpus data, improving upon them, adapting and applying them to multiple languages, and turning them into powerful and flexible techniques capable of supporting massive applications. One focus for improving the scalability and portability of lexical acquisition techniques has been to extend exiting techniques with more powerful, less "supervised" methods. In NLP, the amount of supervision refers to the amount of manual annotation which must be applied to a text corpus before machine learning or other techniques are applied to the data to compile a lexicon. More manual annotation means more accurate training data, and thus a more accurate LR. However, given that it is impractical from a cost and time perspective to manually annotate the vast amounts of data required for multilingual MT across domains, it is important to develop techniques which can learn from corpora with less supervision. Less supervised methods are capable of supporting both large-scale acquisition and efficient domain adaptation, even in the domains where data is scarce. Another focus of lexical acquisition in PANACEA has been the need of LR users to tune the accuracy level of LRs. Some applications may require increased precision, or accuracy, where the application requires a high degree of confidence in the lexical information used. At other times a greater level of coverage may be required, with information about more words at the expense of some degree of accuracy. Lexical acquisition in PANACEA has investigated confidence thresholds for lexical acquisition to ensure that the ultimate users of LRs can generate lexical data from the PANACEA factory at the desired level of accuracy