Articles

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In or- der to address semantic modeling of rela- tional patterns, this paper constructs a new dataset that provides multiple similarity ratings for every pair of relational patterns on the existing dataset (Zeichner et al., 2012). …...

In this paper, with the help of knowl- edge base, we build and formulate a se- mantic space to connect the source and target languages, and apply it to the sequence-to-sequence framework to pro- pose a Knowledge-Based Semantic Em- bedding (KBSE) method. In our KB- SE method, the source sentence …...

The canonical word order of Japanese double object constructions has attracted considerable attention among linguists and has been a topic of many studies. How- ever, most of these studies require either manual analyses or measurements of hu- man characteristics such as brain activities or reading times for each example. Thus, …...

Hand-crafted features based on linguistic and domain-knowledge play crucial role in determining the performance of disease name recognition systems. Such methods are further limited by the scope of these features or in other words, their ability to cover the contexts or word dependencies within a sentence. In this work, we …...

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes exten- sions to structural correspondence learning needed to make it appropriate for the task. For example, we propose a median-based classification instead of the standard binary classification used in previous work. Our results …...

We focus on two leading state-of-the-art approaches to grammatical error correc- tion – machine learning classification and machine translation. Based on the com- parative study of the two learning frame- works and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each …...

Today’s extraction of temporal informa- tion for events heavily depends on an- notated temporal links. These so called TLINKs capture the relation between pairs of event mentions and time expressions. One problem is that the number of possible TLINKs grows quadratic with the num- ber of event mentions, therefore most …...

We discuss an approximate similarity search for word embeddings, which is an operation to approximately find em- beddings close to a given vector. We compared several metric-based search al- gorithms with hash-, tree-, and graph- based indexing from different aspects. Our experimental results showed that a graph-based indexing exhibits robust …...

Entity linking (EL) is the task of dis- ambiguating mentions in text by associ- ating them with entries in a predefined database of mentions (persons, organiza- tions, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other lan- guages, such as …...

Word segmentation is a fundamental task for Chinese language processing. Howev- er, with the successive improvements, the standard metric is becoming hard to distin- guish state-of-the-art word segmentation systems. In this paper, we propose a new psychometric-inspired evaluation metric for Chinese word segmentation, which addresses to balance the very skewed …...

Essay stance classification, the task of de- termining how much an essay’s author agrees with a given proposition, is an important yet under-investigated subtask in understanding an argumentative essay’s overall content. We introduce a new cor- pus of argumentative student essays an- notated with stance information and pro- pose a …...

In this paper we explore the correlation be- tween the sound of words and their mean- ing, by testing if the polarity (‘good guy’ or ‘bad guy’) of a character’s role in a work of fiction can be predicted by the name of the character in the absence of any …...

Solving simple arithmetic word problems is one of the challenges in Natural Lan- guage Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sen- tences to identify the variables and their attributes; and automatically maps …...

Frames defined in FrameNet (FN) share highly similar structures with events in ACE event extraction program. An even- t in ACE is composed of an event trig- ger and a set of arguments. Analogously, a frame in FN is composed of a lexical u- nit and a set of frame …...

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant su- pervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the perfor- mance of relation extraction. To allevi- ate this issue, we propose a sentence-level attention-based model …...

Conventional topic models are ineffec- tive for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence pat- terns. In this work, we organize microblog posts as conversation trees based on re- posting and replying relations, which en- rich context …...

The run time complexity of state-of-the- art inference algorithms in graph-based dependency parsing is super-linear in the number of input words (n). Recently, pruning algorithms for these models have shown to cut a large portion of the graph edges, with minimal damage to the re- sulting parse trees. Solving the …...

To create accessible content for deaf users, we investigate automatically synthesizing animations of American Sign Language (ASL), including grammatically important facial expressions and head movements. Based on recordings of humans perform- ing various types of syntactic face and head movements (which include idiosyn- cratic variation), we evaluate the efficacy of …...

High quality source code is often paired with high level summaries of the compu- tation it performs, for example in code documentation or in descriptions posted in online forums. Such summaries are extremely useful for applications such as code search but are expensive to manually author, hence only done for …...

We propose a new unsupervised learning model, hidden softmax sequence model (HSSM), based on Boltzmann machine for dialogue structure analysis. The model employs three types of units in the hidden layer to discovery dialogue latent struc- tures: softmax units which represent latent states of utterances; binary units which represent latent …...