Articles

This work is an attempt to automatically ob- tain numerical attributes of physical objects. We propose representing each physical object as a feature vector and representing sizes as linear functions of feature vectors. We train the function in the framework of the com- bined regression and ranking with many types …...

We present two simple modifications to the models in the popular Word2Vec tool, in or- der to generate embeddings more suited to tasks involving syntax. The main issue with the original models is the fact that they are insensitive to word order. While order in- dependence is useful for inducing …...

In this paper we present our work on rec- ognizing high level social constructs such as Leadership and Status from textual conversa- tion using an approach that makes use of the background knowledge about social hierarchy and integrates statistical methods and sym- bolic logic based methods. We use a stratified …...

Linguistic similarity is multi-faceted. For in- stance, two words may be similar with re- spect to semantics, syntax, or morphology in- ter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work con- siders guiding word-embeddings with mor- phologically annotated data, …...

In September 2014, Twitter users unequivo- cally reacted to the Ray Rice assault scan- dal by unleashing personal stories of domes- tic abuse via the hashtags #WhyIStayed or #WhyILeft. We explore at a macro-level firsthand accounts of domestic abuse from a substantial, balanced corpus of tweeted in- stances designated with …...

Sparse representations of text such as bag-of- words models or extended explicit semantic analysis (ESA) representations are commonly used in many NLP applications. However, for short texts, the similarity between two such s- parse vectors is not accurate due to the small term overlap. While there have been multiple proposals …...

This paper reports on our research to generate multilingual semantic lexical resources and develop multilingual semantic annotation software, which assigns each word in running text to a semantic category based on a lexical semantic classification scheme. Such tools have an important role in developing intelli- gent multilingual NLP, text mining …...

Multi-document Summarization (MDS) is of great value to many real world applications. Many scoring models are proposed to select appropriate sentences from documents to form the summary, in which the clustering-based methods are popular. In this work, we propose a unified sentence scoring model which mea- sures representativeness and diversity …...

We present a simple, yet effective approach to adapt part-of-speech (POS) taggers to new domains. Our approach only requires a dic- tionary and large amounts of unlabeled tar- get data. The idea is to use the dictionary to mine the unlabeled target data for unambigu- ous word sequences, thus effectively …...

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Gram- mar. In this paper, we present the first LR- style parsing algorithm for Linear Context- Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of …...

An acid test for any new Word Sense Disam- biguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline very hard to beat. Clearly, if WSD researchers had access to MFS values, their striving to bet- ter this heuristic …...

In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowl- edge incorporation. Knowledge incorporation can solve the content sparsity problem effec- tively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the …...

In this paper, we describe the problem of cog- nate identification in NLP. We introduce the idea of gap-weighted subsequences for dis- criminating cognates from non-cognates. We also propose a scheme to integrate phonetic features into the feature vectors for cognate identification. We show that subsequence based features perform better …...

Bootstrapped classifiers iteratively generalize from a few seed examples or prototypes to other examples of target labels. However, sparseness of language and limited supervi- sion make the task difficult. We address this problem by using distributed vector represen- tations of words to aid the generalization. We use the word vectors …...

We present a multi-task learning approach that jointly trains three word alignment mod- els over disjoint bitexts of three languages: source, target and pivot. Our approach builds upon model triangulation, following Wang et al., which approximates a source-target model by combining source-pivot and pivot-target models. We develop a MAP-EM algorithm …...

Two goals are targeted by computer philol- ogy for ancient manuscript corpora: firstly, making an edition, that is roughly speaking one text version representing the whole cor- pus, which contains variety induced through copy errors and other processes and secondly, producing a stemma. A stemma is a graph- based visualization …...

The rapid growth of information sources brings a unique challenge to biographical information extraction: how to find specific facts without having to read all the words. An effective solution is to follow the human scanning strategy which keeps a specific keyword in mind and searches within a specific scope. In …...

We present a novel approach for relation clas- sification, using a recursive neural network (RNN), based on the shortest path between two entities in a dependency graph. Previ- ous works on RNN are based on constituency- based parsing because phrasal nodes in a parse tree can capture compositionality in a …...

We present our work on leveraging multilin- gual parallel corpora of small sizes for Sta- tistical Machine Translation between Japanese and Hindi using multiple pivot languages. In our setting, the source and target part of the corpus remains the same, but we show that using several different pivot to extract …...

Evaluation of segment-level machine transla- tion metrics is currently hampered by: (1) low inter-annotator agreement levels in human as- sessments; (2) lack of an effective mechanism for evaluation of translations of equal quality; and (3) lack of methods of significance testing improvements over a baseline. In this paper, we provide …...