Utsab Barman | Dublin City University (original) (raw)
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Papers by Utsab Barman
Published with ACM as a companion volume to the WWW'13 proceedings, the main track 3 received 13 ... more Published with ACM as a companion volume to the WWW'13 proceedings, the main track 3 received 13 paper submissions, out of which 4 full and 2 short papers were accepted. This was in addition to a poster and demo session, to exhibit practical application in the eld, and foster further discussion about the ways in which data extracted from Microposts is being reused. The accepted submissions cover an array of topics, including a variety of approaches to concept extraction again reinforcing its importance with respect to research on Microposts, among these, rule-based, machine learning and hybrid methods. Other topics covered range from research from a social science perspective, on the use of Microposts to publicise and discuss trending events and topics, and the extraction of intent, meaning and sentiment. Submissions came from 9 countries, with 29% of all authors from institutions in Brazil. Thanks to our local chair, Bernardo Pereira
we present a rule-based answer validation (AV) system based on textual entailment (TE) recognitio... more we present a rule-based answer validation (AV) system based on textual entailment (TE) recognition mechanism that uses semantic features expressed in the Universal Networking Language (UNL). We consider the question as the TE hypothesis (H) and the supporting text as TE text (T). Our proposed TE system compares the UNL relations in both T and H in order to identify the entailment relation as either validated or rejected. For training and evaluation, we used the AVE 2008 development set. We obtained 58% precision and 22% F-score for the decision "validated."
We present a Textual Entailment (TE) recognition system that uses semantic features based on the ... more We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.
Published with ACM as a companion volume to the WWW'13 proceedings, the main track 3 received 13 ... more Published with ACM as a companion volume to the WWW'13 proceedings, the main track 3 received 13 paper submissions, out of which 4 full and 2 short papers were accepted. This was in addition to a poster and demo session, to exhibit practical application in the eld, and foster further discussion about the ways in which data extracted from Microposts is being reused. The accepted submissions cover an array of topics, including a variety of approaches to concept extraction again reinforcing its importance with respect to research on Microposts, among these, rule-based, machine learning and hybrid methods. Other topics covered range from research from a social science perspective, on the use of Microposts to publicise and discuss trending events and topics, and the extraction of intent, meaning and sentiment. Submissions came from 9 countries, with 29% of all authors from institutions in Brazil. Thanks to our local chair, Bernardo Pereira
we present a rule-based answer validation (AV) system based on textual entailment (TE) recognitio... more we present a rule-based answer validation (AV) system based on textual entailment (TE) recognition mechanism that uses semantic features expressed in the Universal Networking Language (UNL). We consider the question as the TE hypothesis (H) and the supporting text as TE text (T). Our proposed TE system compares the UNL relations in both T and H in order to identify the entailment relation as either validated or rejected. For training and evaluation, we used the AVE 2008 development set. We obtained 58% precision and 22% F-score for the decision "validated."
We present a Textual Entailment (TE) recognition system that uses semantic features based on the ... more We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.