This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document...
Previous attempts at identifying translational equivalents in comparable corpora have dealt with very large `general language' corpora and words. We address this task in a sp...
Many recent statistical parsers rely on a preprocessing step which uses hand-written, corpus-specific rules to augment the training data with extra information. For example, head-...
There is no blank to mark word boundaries in Chinese text. As a result, identifying words is difficult, because of segmentation ambiguities and occurrences of unknown words. Conve...
This paper considers several important issues for monolingual and multilingual link detection. The experimental results show that nouns, verbs, adjectives and compound nouns are u...
Systems that interact with the user via natural language are in their infancy. As these systems mature and become more complex, it would be desirable for a system developer if the...
John Chen, Srinivas Bangalore, Owen Rambow, Marily...
This paper attempts to bridge the gap between FrameNet frames and inference. We describe a computational formalism that captures structural relationships among participants in a d...
Nancy Chang, Srini Narayanan, Miriam R. L. Petruck
To support context-based multimodal interpretation in conversational systems, we have developed a semantics-based representation to capture salient information from user inputs an...
We consider here the problem of Base Noun Phrase translation. We propose a new method to perform the task. For a given Base NP, we first search its translation candidates from the...
The relative logical scope of multiple modifiers within NP is often semantically significant. This paper proposes a structurally based method for computing the relative scope of s...