Object detection and pixel-wise scene labeling have both been active research areas in recent years and impressive results have been reported for both tasks separately. The integration of these different types of approaches should boost performance for both tasks as object detection can profit from powerful scene labeling and also pixel-wise scene labeling can profit from powerful object detect...

This paper is concerned with Chinese word segmentation, which is regarded as a character based tagging problem under conditional random field framework. It is different in our method that we consider both feature template selection and tag set selection, instead of feature template focused only method in existing work. Thus, there comes an empirical comparison study of performance among differe...

This paper describes our system in the ALTA shared task 2014. The task is to identify location mentions in Twitter messages, such as place names and point-ofinterests (POIs). We formulated the task as a sequential labelling problem, and explored various features on top of a conditional random field (CRF) classifier. The system achieved 0.726 mean-F measure on the held-out evaluation data. We di...

The paper presents our work in the opinion pilot task in NTCIR6 in Chinese. In extracting opinion holders, we applied Conditional Random Field (CRF) model to find the opinion holders as a sequential labeling task, while in determining the subjectivity and the polarity, we adopted a simple empirical algorithms based on the sentimental dictionary to discriminate the subjective sentences from the ...

Detecting hedges and their scope in natural language text is very important for information inference. In this paper, we present a system based on a cascade method for the CoNLL-2010 shared task. The system composes of two components: one for detecting hedges and another one for detecting their scope. For detecting hedges, we build a cascade subsystem. Firstly, a conditional random field (CRF) ...

This paper describes our system used in the ACL 2015 Workshop on Noisy Usergenerated Text Shared Task for Named Entity Recognition (NER) in Twitter. Our system uses Conditional Random Fields to train two separate classifiers for the two evaluations: predicting 10 fine-grained types, and segmenting named entities. We focus our efforts on generating word representations from large amount of unlab...

We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses the word length, character level (1, 2, 3, 4, and 5)-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We test our proposed framework and compare it to the state-of-theart published syste...

Parsing for clothes in images and videos is a critical step towards understanding the human appearance. In this work, we propose a method to segment clothes in settings where there is no restriction on number and type of clothes, pose of the person, viewing angle, occlusion and number of people. This is a challenging task as clothes, even of the same category, have large variations in color and...

The extraction of individual reference strings from the reference section of scientific publications is an important step in the citation extraction pipeline. Current approaches divide this task into two steps by first detecting the reference section areas and then grouping the text lines in such areas into reference strings. We propose a classification model that considers every line in a publ...

In this paper, we proposed a Chinese word segmentation model for micro-blog text. Although Conditional Random Fields (CRFs) models have been presented to deal with word segmentation, this is still the first time to apply it for the segmentation in the domain of Chinese micro-blog. Different from the genres of common articles, micro-blog has gradually become a new literary with the development o...