Shallow parsing

About: Shallow parsing is a research topic. Over the lifetime, 397 publications have been published within this topic receiving 10211 citations.

Sentence Similarity Measurement Based on Shallow Parsing

Abstract: The paper proposes a novel method to determine sentence similarities. First two compared sentences are parsed by shallow-parsing and all noun phrases, verb phrases and preposition phrases of each sentence are extracted. Then the similarity between each kind of phrases is calculated based on a semantic vector method. The overall sentence similarity is defined as a combination of semantic similarities of the three kinds of phrases. Experiments show that the proposed method has a high performance in F-measure (81.6%) and Recall (97.4%).

Unsupervised chunking based on graph propagation from bilingual corpus.

Abstract: This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms of F-score.

Towards the assessment of free learner's utterances in CALL

Abstract: Computer-Aided Language Learning (CALL) should play an important role in the modern training process because it provides easy accessible, adaptive and flexible ways of learning. This paper addresses the scenario of tutor-learner question answering and attempts to automate the free answers evaluation using the advantages of Natural Language Processing (NLP). Our current approach integrates shallow parsing for analysing the answers and allows the learners to enter various utterances to express themselves. However this variety does not impede the assessment of the student’s answer as we check the utterances against the automatically generated scope of the correct answers. The usage of a “set of answers” instead of one predefined correct answer enables feedback elaboration that helps learners to understand better their knowledge gaps. Briefly, in this paper we show how the combination of shallow and deep semantic NLP techniques can improve the effectiveness of eLearning systems which support communication in free natural language and can make them more satisfactory and pleasant for their users.

Extraction of Opinion Targets Based on Shallow Parsing Features

Abstract: With the rapid development of the world wide web, more and more common users express their opinions on the web and many researchers pay more attentions to sentiment analysis. Fine-grained sentiment analysis on sentence level is very important. The extraction of opinion targets from opinion sentence is the key issue to sentence level of sentiment analysis. To improve the performance of opinion targets extraction, this paper proposes to integrate shallow parsing features and heuristic position information for modeling of the training process without introducing domain lexicon. The experiment results show that after adding the proposed features, nearly all specifications of both conditional random fields and contrast model are improved, and the results of conditional random fields are more efficient than that of the contrast model. Meanwhile, compared with the best results of the 2008 Chinese opinion analysis evaluation, the F measures of conditional random fields are 5 % higher than the maximum value.

Semantics-Driven Shallow Parsing for Chinese Semantic Role Labeling

Abstract: One deficiency of current shallow parsing based Semantic Role Labeling (SRL) methods is that syntactic chunks are too small to effectively group words. To partially resolve this problem, we propose semantics-driven shallow parsing, which takes into account both syntactic structures and predicate-argument structures. We also introduce several new "path" features to improve shallow parsing based SRL method. Experiments indicate that our new method obtains a significant improvement over the best reported Chinese SRL result.