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This paper describes a project tagging a spontaneous speech corpus with morphological information such as word segmentation and parts-ofspeech. We use a morphological analysis sys...
This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Eac...
In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations ge...
Traditional word alignment approaches cannot come up with satisfactory results for Named Entities. In this paper, we propose a novel approach using a maximum entropy model for nam...
We present a formalization of dependency labeling with Integer Linear Programming. We focus on the integration of subcategorization into the decision making process, where the var...
Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and QuestionAnswering system. This paper propos...
: In this paper, we present a spoken language understanding method based on the maximum entropy model. We first extract certain features from the corpus, and then train the maximum...
This paper proposes a chunking strategy to detect unknown words in Chinese word segmentation. First, a raw sentence is pre-segmented into a sequence of word atoms 1 using a maximum...