Showing papers by "Parma Nand published in 2014"

The Role of Linked Data in Content Selection

Abstract: This paper explores the appropriateness of utilizing Linked Data as a knowledge source for content selection. Content Selection is a crucial subtask in Natural Language Generation which has the function of determining the relevancy of contents from a knowledge source based on a communicative goal. The recent online era has enabled us to accumulate extensive amounts of generic online knowledge some of which has been made available as structured knowledge sources for computational natural language processing purposes. This paper proposes a model for content selection by utilizing a generic structured knowledge source, DBpedia, which is a replica of the unstructured counterpart, Wikipedia. The proposed model uses log likelihood to rank the contents from DBpedia Linked Data for relevance to a communicative goal. We performed experiments using DBpedia as the Linked Data resource using two keyword datasets as communicative goals. To optimize parameters we used keywords extracted from QALD-2 training dataset and QALD-2 testing dataset is used for the testing. The results was evaluated against the verbatim based selection strategy. The results showed that our model can perform 18.03% better than verbatim selection.

Real Text-CS - Corpus Based Domain Independent Content Selection Model

Abstract: Content selection is a highly domain dependent task responsible for retrieving relevant information from a knowledge source using a given communicative goal. This paper presents a domain independent content selection model using keywords as communicative goal. We employ DBpedia triple store as our knowledge source and triples are selected based on weights assigned to each triple. The calculation of the weights is carried out through log likelihood distance between a domain corpus and a general reference corpus. The method was evaluated using keywords extracted from QALD dataset and the performance was compared with cross entropy based statistical content selection. The evaluation results showed that the proposed method can perform 32% better than cross entropy based statistical content selection.

Interaction History Based Answer Formulation for Question Answering

Abstract: With the rapid growth in information access methodologies, question answering has drawn considerable attention among others. Though question answering has emerged as an interesting new research domain, still it is vastly concentrated on question processing and answer extraction approaches. Latter steps like answer ranking, formulation and presentations are not treated in depth. Weakness we found in this arena is that answers that a particular user has acquired are not considered, when processing new questions. As a result, current systems are not capable of linking two questions such as “When is the Apple founded?” with a previously processed question “When is the Microsoft founded?” generating an answer in the form of “Apple is founded one year later Microsoft founded, in 1976”. In this paper we present an approach towards question answering to devise an answer based on the questions already processed by the system for a particular user which is termed as interaction history for the user. Our approach is a combination of question processing, relation extraction and knowledge representation with inference models. During the process we primarily focus on acquiring knowledge and building up a scalable user model to formulate future answers based on current answers that same user has processed. According to evaluation we carried out based on the TREC resources shows that proposed technology is promising and effective in question answering.

A HMM POS Tagger for Micro-blogging Type Texts

Abstract: The high volume of communication via micro-blogging type messages has created an increased demand for text processing tools customised the unstructured text genre. The available text processing tools developed on structured texts has been shown to deteriorate significantly when used on unstructured, micro-blogging type texts. In this paper, we present the results of testing a HMM based POS (Part-Of-Speech) tagging model customized for unstructured texts. We also evaluated the tagger against published CRF based state-of-the-art POS tagging models customized for Tweet messages using three publicly available Tweet corpora. Finally, we did cross-validation tests with both the taggers by training them on one Tweet corpus and testing them on another one.

A Multi-Strategy Approach for Location Mining in Tweets: AUT NLP Group Entry for ALTA-2014 Shared Task

Abstract: This paper describes the strategy and the results of a location mining system used for the ALTA-2014 shared task competition. The task required the participants to identify the location mentions in 1003 Twitter test messages given a separate annotated training set of 2000 messages. We present an architecture that uses a basic named entity recognizer in conjunction with various rule-based modules and knowledge infusion to achieve an average F score of 0.747 which won the second place in the competition. We used the pre-trained Stanford NER which gives us an F score of 0.532 and used an ensemble of other techniques to reach the 0.747 value. The other major source of location resolver was the DBpedia location list which was used to identify a large percentage of locations with an individual F-score of 0.935.