3,155 research outputs found
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
Spoken language processing requires speech and natural language integration.
Moreover, spoken Korean calls for unique processing methodology due to its
linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic
spoken Korean processing engine, which emphasizes that: 1) connectionist and
symbolic techniques must be selectively applied according to their relative
strength and weakness, and 2) the linguistic characteristics of Korean must be
fully considered for phoneme recognition, speech and language integration, and
morphological/syntactic processing. The design and implementation of SKOPE
demonstrates how connectionist/symbolic hybrid architectures can be constructed
for spoken agglutinative language processing. Also SKOPE presents many novel
ideas for speech and language processing. The phoneme recognition,
morphological analysis, and syntactic analysis experiments show that SKOPE is a
viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be
presented at IJCAI95 workshops on new approaches to learning for natural
language processin
Compositional Morphology for Word Representations and Language Modelling
This paper presents a scalable method for integrating compositional
morphological representations into a vector-based probabilistic language model.
Our approach is evaluated in the context of log-bilinear language models,
rendered suitably efficient for implementation inside a machine translation
decoder by factoring the vocabulary. We perform both intrinsic and extrinsic
evaluations, presenting results on a range of languages which demonstrate that
our model learns morphological representations that both perform well on word
similarity tasks and lead to substantial reductions in perplexity. When used
for translation into morphologically rich languages with large vocabularies,
our models obtain improvements of up to 1.2 BLEU points relative to a baseline
system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning
(ICML
Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR
The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light
term conation step and useful in case of few language-specific resources. For English, the corpusbased
stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR.
Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from
selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness
compared to using a fixed number of terms for different languages
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning word representations from character and morpheme
level word decompositions. We incorporate these representations in a novel
machine translation model which jointly learns word alignments and translations
via a hard attention mechanism. Evaluating on translating from several
morphologically rich languages into English, we show consistent improvements
over strong baseline methods, of between 1 and 1.5 BLEU points
The Role of Visual Acuity and Segmentation Cues in Compound Word Identification
Studies are reviewed that demonstrate how the identification of compound words during reading is constrained by the foveal area of the eye. When compound words are short, their letters can be identified during a single fixation, leading to the whole-word route dominating word recognition from early on. Hence, marking morpheme boundaries visually by means of hyphens slows down the processing of short words by encouraging morphological decomposition when holistic processing is a feasible option. In contrast, the decomposition route dominates the early stages of identifying long compound words. Thus, visual marking of morpheme boundaries facilitates processing of long compound words, unless the initial fixation made on the word lands very close to the morpheme boundary. The reviewed pattern of results is explained by the visual acuity principle (Bertram and Hyönä, 2003) and the dual-route framework of morphological processing
Assessing the effect of ambiguity in compositionality signaling on the processing of diphones
Consonantal diphones differ as to their ambiguity (whether or not they indicate
morphological complexity reliably by occurring exclusively either within or across morphemes)
and lexicality (how frequently they occur within morphemes rather than across
morpheme boundaries). This study empirically investigates the influence of ambiguity and
lexicality on the processing speed of consonantal diphones in speech perception. More
specifically, its goal is to test the predictions of the Strong Morphonotactic Hypothesis,
which asserts that phonotactic processing is influenced by morphological structure, and to
clarify the two conceptions thereof present in extant research. In two discrimination task
experiments, it is found that the processing speed of cross-morpheme diphones decreases
with their ambiguity, but there is no processing difference between primarily crossmorphemic
and morpheme-internal diphones. We conclude that the predictions of the
Strong Morphonotactic Hypothesis are borne out only partially, and we discuss the
discrepancies
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