Articles

In conversation, speakers tend to “ac- commodate” or “align” to their partners, changing the style and substance of their communications to be more similar to their partners’ utterances. We focus here on “linguistic alignment,” changes in word choice based on others’ choices. Although linguistic alignment is observed across many different …...

Most current chatbot engines are designed to reply to user utterances based on exist- ing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chat- bot engines that can leverage unstructured documents, instead of Q-R pairs, to re- spond to utterances. A learning …...

Most sentence embedding models typical- ly represent each sentence only using word surface, which makes these models indis- criminative for ubiquitous homonymy and polysemy. In order to enhance represen- tation capability of sentence, we employ conceptualization model to assign associ- ated concepts for each sentence in the tex- t corpus, …...

Automatic negation scope detection is a task that has been tackled using differ- ent classifiers and heuristics. Most sys- tems are however 1) highly-engineered, 2) English-specific, and 3) only tested on the same genre they were trained on. We start by addressing 1) and 2) using a neural network architecture. …...

Traditional approaches to extractive summarization rely heavily on human- engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general frame- work for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us …...

Question answering requires access to a knowledge base to check facts and rea- son about information. Knowledge in the form of natural language text is easy to ac- quire, but difficult for automated reason- ing. Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire. In this paper we …...

Passage-level question answer matching is a challenging task since it requires effec- tive representations that capture the com- plex semantic relations between questions and answers. In this work, we propose a series of deep learning models to address passage answer selection. To match pas- sage answers to questions accommodat- ing …...

Cognitive science researchers have em- phasized the importance of ordering a complex task into a sequence of easy to hard problems. Such an ordering provides an easier path to learning and increases the speed of acquisition of the task com- pared to conventional learning. Recent works in machine learning have …...

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architec- ture which generates an output sequence conditioned on an arbitrary number of in- put functions. Crucially, our approach allows both the choice of conditioning context and the …...

Broad domain question answering is of- ten difficult in the absence of structured knowledge bases, and can benefit from shallow lexical methods (broad coverage) and logical reasoning (high precision). We propose an approach for incorporating both of these signals in a unified frame- work based on natural logic. We extend …...

Understanding unstructured text is a ma- jor goal within natural language process- ing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we in- vestigate machine comprehension on the challenging MCTest benchmark. Partly because of its limited size, prior work on MCTest has …...

AbstractThis paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities …...

AbstractIdentification of Multi-Word Expressions (MWEs) lies at the heart of many natural language processing applications. In this research, we deal with a particular type of Hebrew MWEs, Verb-Noun MWEs (VN-MWEs), which combine a verb and a noun with or without other words. Most prior work on MWEs classification focused on …...

AbstractSemantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two …...

AbstractIn community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we …...

AbstractWe introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses characters level 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 …...

AbstractWe present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to …...

AbstractThere are growing needs for patent analysis using Natural Language Processing (NLP)-based approaches. Although NLP-based approaches can extract various information from patents, there are very few approaches proposed to extract those parts what inventors regard as novel or having an inventive step compared to all existing works ever. To extract …...

AbstractOne of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL’2003 news dataset. For instance, the biggest Ritter tweet corpus is only 45,000 tokens – a mere 15% …...

AbstractThis paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is made through the enrichment of a well-known MT evaluation metric: METEOR. METEOR enables an approximate match (synonymy or morphological similarity) between an automatic and a reference …...