Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon a...

Development of lexical resources is, along with grammar development, one of the main efforts when building multilingual NLP applications. In this paper, we present a tool-based approach for more efficient manual lexicon development for a spoken language translation system. The approach in particular addresses the common problems of multilingual lexica including the redundancy of encoded informa...

In this paper we describe an approach to an adaptive mor phological analysis based on lexicon corpus acquired from Internet We focus on automating categorization words into a morphological paradigm in exive languages It is done by inducing possible word forms using morphological knowledge base and by looking for word forms of possible in ections in a morphological lexicon We developed a prototy...

In natural language, the meaning of a lexeme often varies due to the specific surrounding context. Computational approaches to natural language processing can benefit from a reliable, long-range-context-dependent representation of the meaning of each lexeme that appears in a given sentence. We have developed a general new technique that produces a context-dependent ‘meaning’ representation for ...

This investigation addressed the questions at what rate and how an elderly Japanese woman, AA, lost kanji nouns between the ages of 83 years and 93 years. Results from a kanji naming task given twice with a 10-year interval showed that (1) on average, the size of AA's mental lexicon of kanji shrank at a rate of approximately 1% per year and (2) while ease of acquisition had the highest correlat...

Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain and stock market. In this paper, we present a new approach to learning stock market lexicon from StockTwits, a popular financial social network for investors to share ...

We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and...

Sentiment analysis aims to identify and categorize customer’s opinion and judgments using either traditional supervised learning techniques or unsupervised approaches. Traditionally, Sentiment Analysis is performed using machine learning techniques such as a naive Bayes classification or support vector machines (SVM), or could make use of a sentiment lexicon, that is, a list of words that are m...

In this paper, we detail a method for domain specific, multi-category emotion recognition, based on human computation. We create an Amazon Mechanical Turk1 task that elicits emotion labels and phrase-emotion associations from the participants. Using the proposed method, we create an emotion lexicon, compatible with the 20 emotion categories of the Geneva Emotion Wheel. GEW is the first computat...

This paper describes a lexicon-driven approach to on-line handwritten Japanese disease name recognition using a time-synchronous method. A trie lexicon is constructed from a disease name database containing 21,713 disease name phrases. It expands the search space using time-synchronous method and applies the beam search strategy to search segmentation candidate lattice constructed based on prim...