Articles

Research "into the automatic acquisition of subcategorization frames (SCFS) from corpora is starting to produce large-scale computa- tional lexicons which include valuable fre- quency information. However, the accuracy of the resulting lexicons shows room for im- provement. One significant source of error lies in the statistical filtering used by some …...

; This paper presents a query tool for syntacti- caUy ~.nnotated corpora. The query tool is de- veloped to search the Verbmobil treebanks an- notated at the University of Tfibingen. How- ever, in principle it also can be adapted to other corpora such as the Negra Corpus, the Penn Treebank …...

This paper describes a set of experiments car- ried out to explore the domain dependence of alternative supervised Word Sense Disam- biguation algorithms. The aim of the work is threefold: studying the performance of these algorithms when tested on a different cor- pus from that they were trained on; explor- …...

Long sentence analysis has been a critical problem because of high complexity. This pa- per addresses the reduction of parsing com- plexity by intra-sentence segmentation, and presents maximum entropy model for deter- mining segmentation positions. The model features lexical contexts of segmentation posi- tions, giving a probability to each potential …...

This paper describes a first attempt at a sta- tistical model for simultaneous syntactic pars- ing and generalized word-sense disambigna- tion. On a new data set we have constructed for the task, while we were disappointed not to find parsing improvement over a traditional parsing model, our model achieves a …...

This paper introduces an intuitive search environment for casual and novice Chinese users over Internet. The system consists of four components, a concept network, a query reformulation model, a standard search engine, and an automatic summarizer. When the user enters one or more fairly general and vague terms, the search …...

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We eval- uated our approach on the Remedia data set, a common data set used in several recent pa- pers on the reading comprehension task. Our learning approach achieves accuracy competi- …...

This paper presents the automatic construction of a Korean WordNet from pre-existing lexical resources. A set of automatic WSD techniques is described for linking Korean words collected from a bilingual MRD to English WordNet synsets. We will show how individual linking provided by each WSD method is then combined to …...

The development of natural language inter- faces (NLI's) for databases has been a chal- lenging problem in natural language process- ing (NLP) since the 1970's. The need for NLI's has become more pronounced due to the widespread access to complex databases now available through the Internet. A challenging problem for …...

We propose an empirical method for estimating term weights directly from relevance judgements, avoiding various standard but potentially trouble- some assumptions. It is common to assume, for ex- ample, that weights vary with term frequency (t f) and inverse document frequency (idf) in a particu- lar way, e.g., tf. idf, …...

In this paper, a method for the word alignment of English-Chinese corpus based on chunks is proposed. The chunks of English sentences are identified firstly. Then the chunk boundaries of Chinese sentences are predicted by the translations of English chunks and heuristic information. The ambiguities of Chinese chunk boundaries are …...

This paper proposed a new query translation method based on the mutual information matrices of terms in the Chinese and English corpora. Instead of looking up a • bilingual phrase dictionary, the compositional phrase (the translation of phrase can be derived from the translation of its components) in the query …...

This paper presents empirical results in cross-lingual information retrieval using English queries to access Chinese documents (TREC-5 and TREC-6) and Spanish documents (TREC-4). Since our interest is in languages where resources may be minimal, we use an integrated probabilistic model that requires only a bilingual dictionary as a resource. We …...

The bigram language models are popular, in much language processing applications, in both Indo-European and Asian languages. However, when the language model for Chinese is applied in a novel domain, the accuracy is reduced significantly, from 96% to 78% in our evaluation. We apply pattern recognition techniques (i.e. Bayesian, decision …...

This paper presents a novel nonlocal lmlguage model which utilizes contextual information. A reduced vector space model calculated from co-occurrences of word pairs provides word co-occurrence vectors. The sum of word co- occurrence vectors represents tile context of a document, and the cosine similarity between the context vector and the …...

This paper proposes a new error-driven HMM- based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger uses a new Hidden Markov Modelling approach which incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to decrease the memory requirement by …...

This paper presents results for a maximum- entropy-based part of speech tagger, which achieves superior performance principally by enriching the information sources used for tagging. In particular, we get improved results by incorporating these features: (i) more extensive treatment of capitaliza- tion for unknown words; (ii) features for the disambiguation …...

Grammars are core elements of many NLP ap- plications. In this paper, we present a system that automatically extracts lexicalized gram- mars from annotated corpora. The data pro- duced by this system have been used in sev- eral tasks, such as training NLP tools (such as Supertaggers) and estimating the …...

We address the issue of 'topic analysis,' by which is determined a text's topic structure, which indicates what topics are included in a text, and how topics change within the text. We propose a novel approach to this issue, one based on statistical modeling and learning. We represent topics by …...

Transformation-based learning has been success- fully employed to solve many natural language processing problems. It has many positive fea- tures, but one drawback is that it does not provide estimates of class membership probabilities. In this paper, we present a novel method for obtaining class membership probabilities from a transformation-based …...