Chinese text chunking using lexicalized HMMS

This paper presents a lexicalized HMM-based approach to Chinese text chunking. To tackle the problem of unknown words, we formalize Chinese text chunking as a tagging task on a sequence of known words. To do this, we employ the uniformly lexicalized HMMs and develop a lattice-based tagger to assign each known word a proper hybrid tag, which involves four types of information: word boundary, POS, chunk boundary and chunk type. In comparison with most previous approaches, our approach is able to integrate different features such as part-of-speech information, chunk-internal cues and contextual information for text chunking under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. Our preliminary experiments on the PolyU Shallow Treebank show that the use of lexicalization technique can substantially improve the performance of a HMM-based chunking system. © 2005 IEEE.published_or_final_versio

Chinese unknown word identification as known word tagging

This paper presents a tagging approach to Chinese unknown word identification based on lexicalized hidden Markov models (LHMMs). In this work, Chinese unknown word identification is represented as a tagging task on a sequence of known words by introducing word-formation patterns and part-of-speech. Based on the lexicalized HMMs, a statistical tagger is further developed to assign each known word an appropriate tag that indicates its pattern in forming a word and the part-of-speech of the formed word. The experimental results on the Peking University corpus indicate that the use of lexicalization technique and the introduction of part-of-speech are helpful to unknown word identification. The experiment on the SIGHAN-PK open test data also shows that our system can achieve state-of-art performance.published_or_final_versio

Supertagged phrase-based statistical machine translation

Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system performance to deteriorate. In this work we show that incorporating lexical syntactic descriptions in the form of supertags can yield significantly better PBSMT systems. We describe a novel PBSMT model that integrates supertags into the target language model and the target side of the translation model. Two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. Despite the differences between these two approaches, the supertaggers give similar improvements. In addition to supertagging, we also explore the utility of a surface global grammaticality measure based on combinatory operators. We perform various experiments on the Arabic to English NIST 2005 test set addressing issues such as sparseness, scalability and the utility of system subcomponents. Our best result (0.4688 BLEU) improves by 6.1% relative to a state-of-theart PBSMT model, which compares very favourably with the leading systems on the NIST 2005 task

Chinese named entity recognition using lexicalized HMMs

This paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we first employ a known-word bigram-based model to segment a sentence into a sequence of known words, and then apply the uniformly lexicalized HMMs to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity. Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. We have tested our system using different public corpora. The results show that lexicalized HMMs can substantially improve NER performance over standard HMMs. The results also indicate that character-based tagging (viz. the tagging based on pure single-character words) is comparable to and can even outperform the relevant known-word based tagging when a lexicalization technique is applied.postprin

Syntactic phrase-based statistical machine translation

Phrase-based statistical machine translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 3.51% relative) on the NIST 2003 Arabic-English test corpus

Three New Probabilistic Models for Dependency Parsing: An Exploration

After presenting a novel O(n^3) parsing algorithm for dependency grammar, we develop three contrasting ways to stochasticize it. We propose (a) a lexical affinity model where words struggle to modify each other, (b) a sense tagging model where words fluctuate randomly in their selectional preferences, and (c) a generative model where the speaker fleshes out each word's syntactic and conceptual structure without regard to the implications for the hearer. We also give preliminary empirical results from evaluating the three models' parsing performance on annotated Wall Street Journal training text (derived from the Penn Treebank). In these results, the generative (i.e., top-down) model performs significantly better than the others, and does about equally well at assigning part-of-speech tags.Comment: 6 pages, LaTeX 2.09 packaged with 4 .eps files, also uses colap.sty and acl.bs

Modeling Dependencies in Natural Languages with Latent Variables

In this thesis, we investigate the use of latent variables to model complex dependencies in natural languages. Traditional models, which have a fixed parameterization, often make strong independence assumptions that lead to poor performance. This problem is often addressed by incorporating additional dependencies into the model (e.g., using higher order N-grams for language modeling). These added dependencies can increase data sparsity and/or require expert knowledge, together with trial and error, in order to identify and incorporate the most important dependencies (as in lexicalized parsing models). Traditional models, when developed for a particular genre, domain, or language, are also often difficult to adapt to another. In contrast, previous work has shown that latent variable models, which automatically learn dependencies in a data-driven way, are able to flexibly adjust the number of parameters based on the type and the amount of training data available. We have created several different types of latent variable models for a diverse set of natural language processing applications, including novel models for part-of-speech tagging, language modeling, and machine translation, and an improved model for parsing. These models perform significantly better than traditional models. We have also created and evaluated three different methods for improving the performance of latent variable models. While these methods can be applied to any of our applications, we focus our experiments on parsing. The first method involves self-training, i.e., we train models using a combination of gold standard training data and a large amount of automatically labeled training data. We conclude from a series of experiments that the latent variable models benefit much more from self-training than conventional models, apparently due to their flexibility to adjust their model parameterization to learn more accurate models from the additional automatically labeled training data. The second method takes advantage of the variability among latent variable models to combine multiple models for enhanced performance. We investigate several different training protocols to combine self-training with model combination. We conclude that these two techniques are complementary to each other and can be effectively combined to train very high quality parsing models. The third method replaces the generative multinomial lexical model of latent variable grammars with a feature-rich log-linear lexical model to provide a principled solution to address data sparsity, handle out-of-vocabulary words, and exploit overlapping features during model induction. We conclude from experiments that the resulting grammars are able to effectively parse three different languages. This work contributes to natural language processing by creating flexible and effective latent variable models for several different languages. Our investigation of self-training, model combination, and log-linear models also provides insights into the effective application of these machine learning techniques to other disciplines