86 citations · 110 across the 5 of their papers we have counts for
8 papers
Syllable based DNN-HMM Cantonese Speech to Text System
Timothy Wong, Claire Li, Sam Lam +6
This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid…
Dual Memory Network Model for Biased Product Review Classification
Yunfei Long, Mingyu Ma, Qin Lu +2
In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified mod…
A Question Answering Approach to Emotion Cause Extraction
Lin Gui, Jiannan Hu, Yulan He +3
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by r…
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can…
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 unsu…
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
Enrico Santus, Alessandro Lenci, Tin-Shing Chiu +2
ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies o…