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enhances the machine learning based Stanford CoreNLP Part-of-Speech (POS) tagger with the Twitter model to extract essential keywords from a tweet. The system was enhanced using two rule-based parsers and a corpus. The research was conducted using tweets of customer service requests sent to a telecommunication company. A
stylistically from that of keyword search queries. In this paper, we propose a machine translation approach to learn a mapping from natural language utterances to search queries. We train statistical translation models, using task and domain independent semantically equivalent natural language and keyword search query pairs mined
YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and
In this paper we propose an approach for Chinese question analysis and answer extraction. A general question analysis process contains keyword extraction and question classification. Question classification plays a crucial role in automatic question answering. To implement the question classification, we have carried
To support understanding of news, we propose a novel TEC model (Topic-Event Causal relation model) and describe the method to construct a Causal Network in the TEC model. The model includes two types of keywords to represent casual relations: topic keywords, which describe topics, and event keywords, which describe
factorization with concept-based features is significantly lower than the error with standard keyword-based features. Qualitative evaluations also suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using standard keyword-based features.
, these keyword-based methods can not support spatial query very well. For example, searching documents on “debris flow took place in Hunan last year”, the documents selected in this way may only contain the words “debris flow” and “Hunan” rather than refer to “debris flow
It is necessary that sophisticated opinion extraction methods to be used, since ordinary keyword search does not suit for mining varied opinions. Assessment, proposed emotional correspondence or compelling state are might be his or her assessment is attitude. Classifying polarity of an unstructured document text in
With the rapid growth of web services, web services discovery becomes exceedingly important and challenging. Currently, many discovery approaches have been proposed such as keyword-based or VSM-based syntactic matching and ontology-based semantic matching. Syntactic matching approaches are clearly insufficient due to
keywords from a text and use the trained model to generate similarities among these keywords. Since the word2vec model maps the relations of terms into a semantic space, the similarity of the terms is given by cosine similarity of the vectors. We construct the graph of these terms and its adjacency matrix. Finally, spectral
generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant 36% boost in performance
relation extraction based on bootstrapping, but it did not consider the role of the keywords in the semantic relation. This paper presents an improved context pattern, which has a stronger semantic expressiveness, which is used to extract semantic relations and makes the semantic relation extraction more accurate. First of
Feature location is a human-oriented and information-intensive process. When performing feature location tasks with existing tools, developers often feel it difficult to formulate an accurate feature query (e.g., keywords) and determine the relevance of returned results. In this paper, we propose a feature location
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