The Experts below are selected from a list of 18363 Experts worldwide ranked by ideXlab platform
Jiři Hana - One of the best experts on this subject based on the ideXlab platform.
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multilinguality in a text generation system for three slavic languages
International Conference on Computational Linguistics, 2000Co-Authors: Geertjan M Kruijff, Elke Teich, John A Bateman, Ivana Kruijffkorbayova, Hana Skoumalova, Serge Sharoff, Lena Sokolova, Tony Hartley, Kamenka Staykova, Jiři HanaAbstract:This paper describes a multilingual text generation system in the domain of CAD/CAM software instructions for Bulgarian, Czech and Russian. Starting from a language-independent semantic representation, the system drafts natural, continuous text as typically found in software manuals. The core modules for strategic and tactical generation are implemented using the KPML platform for Linguistic Resource development and generation. Prominent characteristics of the approach implemented are a treatment of multilinguality that makes maximal use of the commonalities between languages while also accounting for their differences and a common representational strategy for both text planning and sentence generation.
Geertjan M Kruijff - One of the best experts on this subject based on the ideXlab platform.
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multilinguality in a text generation system for three slavic languages
International Conference on Computational Linguistics, 2000Co-Authors: Geertjan M Kruijff, Elke Teich, John A Bateman, Ivana Kruijffkorbayova, Hana Skoumalova, Serge Sharoff, Lena Sokolova, Tony Hartley, Kamenka Staykova, Jiři HanaAbstract:This paper describes a multilingual text generation system in the domain of CAD/CAM software instructions for Bulgarian, Czech and Russian. Starting from a language-independent semantic representation, the system drafts natural, continuous text as typically found in software manuals. The core modules for strategic and tactical generation are implemented using the KPML platform for Linguistic Resource development and generation. Prominent characteristics of the approach implemented are a treatment of multilinguality that makes maximal use of the commonalities between languages while also accounting for their differences and a common representational strategy for both text planning and sentence generation.
Pushpak Bhattacharyya - One of the best experts on this subject based on the ideXlab platform.
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sentiment analysis in twitter with lightweight discourse analysis
International Conference on Computational Linguistics, 2012Co-Authors: Subhabrata Mukherjee, Pushpak BhattacharyyaAbstract:We propose a lightweight method for using discourse relations for polarity detection of tweets . This method is targeted towards the web-based appli cations that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy Linguistic Resource s like parsing due to frequent failure of the parsers to handle noisy dat a. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discours e particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate discourse information in any bag-of-words model, to improve sentiment classification accuracy. We also probe the influenc e of the semantic operators like modals and negations on the discourse relations that affect the sentime nt of a sentence. Discourse relations and corresponding rules are identified with minimal processing - just a list look up. We first give a Linguistic description of the various discourse r elations which leads to conditions in rules and features in SVM. We show that our discourse-based bag-of-words model performs well in a noisy medium ( Twitter ), where it performs better than an existing Twitte r-based application. Furthermore, we show that our approach is beneficia l to structured reviews as well, where we achieve a better accuracy than a state-of-the-art s ystem in the travel review domain. Our system compares favorably with the state-of-the-art system s and has the additional attractiveness of being less Resource intensive.
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hindi pos tagger using naive stemming harnessing morphological information without extensive Linguistic knowledge
2008Co-Authors: Manish Shrivastava, Pushpak BhattacharyyaAbstract:Part of Speech tagging for Indian Languages in general and Hindi in particular is not a very widely explored territory. There have been many attempts at developing a good POS tagger for Hindi, but the morphological complexity of the language makes it a hard nut to crack. Some of the best taggers available for Indian Languages employ hybrids of machine learning or stochastic methods and Linguistic knowledge. Though, the results achieved using such methods are good, there practicability for other inflective Indian Languages is reduced due to their heavy dependence on Linguistic knowledge. Even though taggers can achieve very good results if provided good morphological information, the cost of creating these Resources renders such methods impractical. In this paper, we present a simple HMM based POS tagger, which employs a naive(longest suffix matching) stemmer as a pre-processor to achieve reasonably good accuracy of 93.12%. This method does not require any Linguistic Resource apart from a list of possible suffixes for the language. This list can be easily created using existing machine learning techniques. The aim of this method is to demonstrate that even without employing tools like morphological analyzer or Resources like a pre-compiled structured lexicon, it is possible to harness the morphological richness of Indian Languages.
Yi Zhang - One of the best experts on this subject based on the ideXlab platform.
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disambiguating compound nouns for a dynamic hpsg treebank of wall street journal texts
Language Resources and Evaluation, 2010Co-Authors: Valia Kordoni, Yi ZhangAbstract:The aim of this paper is twofold. We focus, on the one hand, on the task of dynamically annotating English compound nouns, and on the other hand we propose disambiguation methods and techniques which facilitate the annotation task. Both the aforementioned are part of a larger on-going effort which aims to create HPSG annotation for the texts from theWall Street Journal (henceforward WSJ) sections of the Penn Treebank (henceforward PTB) with the help of a hand-written large-scale and wide-coverage grammar of English, the English Resource Grammar (henceforward ERG; Flickinger (2002)). As we show in this paper, such annotations are very rich Linguistically, since apart from syntax they also incorporate semantics, which does not only ensure that the treebank is guaranteed to be a truly sharable, re-usable and multi-functional Linguistic Resource, but also calls for the necessity of a better disambiguation of the internal (syntactic) structure of larger units of words, such as compound nouns, since this has an impact on the representation of their meaning, which is of utmost interest if the Linguistic annotation of a given corpus is to be further understood as the practice of adding interpretative Linguistic information of the highest quality in order to give added value to the corpus.
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annotating wall street journal texts using a hand crafted deep Linguistic grammar
Linguistic Annotation Workshop, 2009Co-Authors: Valia Kordoni, Yi ZhangAbstract:This paper presents an on-going effort which aims to annotate the Wall Street Journal sections of the Penn Treebank with the help of a hand-written large-scale and wide-coverage grammar of English. In doing so, we are not only focusing on the various stages of the semi-automated annotation process we have adopted, but we are also showing that rich Linguistic annotations, which can apart from syntax also incorporate semantics, ensure that the treebank is guaranteed to be a truly sharable, re-usable and multi-functional Linguistic Resource.
Elke Teich - One of the best experts on this subject based on the ideXlab platform.
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multilinguality in a text generation system for three slavic languages
International Conference on Computational Linguistics, 2000Co-Authors: Geertjan M Kruijff, Elke Teich, John A Bateman, Ivana Kruijffkorbayova, Hana Skoumalova, Serge Sharoff, Lena Sokolova, Tony Hartley, Kamenka Staykova, Jiři HanaAbstract:This paper describes a multilingual text generation system in the domain of CAD/CAM software instructions for Bulgarian, Czech and Russian. Starting from a language-independent semantic representation, the system drafts natural, continuous text as typically found in software manuals. The core modules for strategic and tactical generation are implemented using the KPML platform for Linguistic Resource development and generation. Prominent characteristics of the approach implemented are a treatment of multilinguality that makes maximal use of the commonalities between languages while also accounting for their differences and a common representational strategy for both text planning and sentence generation.