Group items tagged

"Computational Linguistics became an open access journal, freely available to all online readers. ... Computational Linguistics is the longest running publication devoted exclusively to the design and analysis of natural language processing systems. From this highly-regarded quarterly, university and industry linguists, speech specialists, and philosophers get information about computational aspects of research on language, linguistics, and the psychology of language processing and performance."

Over the past few months, the online word game Wordle has skyrocketed in popularity, with cryptic grids of gray, green and yellow squares appearing on social media.The game is challenging, but simple: Once a day, players have six guesses to identify a new five-letter word (all players receive the same word on a given day). Each guess provides color-coded hints: a letter turns green if it is in the correct spot, yellow if it is part of the word but in a different spot, and gray if it is not in the word at all. In Wordle, the process of intuiting a target word from color-coded clues provides a window into our subconscious understanding of how language works, according to UChicago linguist Jason Riggle. In effect, it turns everyone into a linguist, forcing us to wrestle with sound fragments and stitch them together according to probability distributions.

This article talks about a study that was conducted by MIT linguistics professors and a group from Carleton University that explored the phenomenon of why children mix up the words "and" and "or." Linguists say that children use almost entirely the same approach as adults when it comes to evaluating potentially ambiguous sentences, by testing and "strengthening" them into sentences with more precise meanings, when disjunction and conjunction ("or" and "and") are involved. However, they found that children do not test how a sentence would change if "and" was directly substituted for "or." On the other hand, adults compute "scalar implicatures," which is a technical phrase for thinking about the implications of the logical relationship between a sentence and its alternative. The research team conducted the study's experiment by testing 59 English-speaking children and 26 adults. The children ranged in age from 4 months to 6 years. The linguists gave the subjects a series of statements along with pictures, and asked them to say whether the statements were true or false. The results suggest that children are computing scalar implicatures when they evaluate the statements, but they largely do not substitute disjunctions and conjunctions when testing out the possible meaning of sentences, as adults do. In general, the researchers observed, across languages, and for children and adults alike, when you take 'and' out of the space of alternatives, "or" becomes "and."

This article provides an insight into an interview with Christopher Manning, a Stanford professor of computer science and linguistics. He is focused on computational linguistics, also known as natural language processing. Natural language processing involves creating algorithms that can allow computers to understand written and spoken language and then intelligently respond. This involves systems such as Siri, Alexa, and Google Voice. These systems are pretty advanced technology, however, they are still far from perfect. Manning notes that people will probably still be working on natural learning processing in twenty years.

more uses for computational linguistics

Although linguists have used for many years the process of deriving pronunciations of obscure words via comparing it to related descendant languages, this method has now been expedited by being scripted into computer code. Thus, scientists can now utilize computer technology to predict pronunciations of obscure words with up to 76% accuracy, which is greatly assisting the documentation of ancient, poorly recorded, or endangered languages in India.

In this essay, D.E. Whittkower, in the Dept. of Philosophy at Old Dominion University, attempts to establish that social media communications constitute a secondary literacy, which shares many of the features of spoken language ("orality"). Whittkower's discussion of Facebook, in particular, is thought-provoking; he suggests that the site is a "remarkably well-suited site for the activity of friendship," providing opportunities for relationships and interests to grow and intensify and for participants to engage in linguistic and post-linguistic social grooming.

"As the world's leading linguist, what are your thoughts on Watson, the robot that will be appearing on "Jeopardy"?"

Article talks about the growing field of forensic linguistics: that is, the study of language to identify who said or wrote that verbal sample. Applications are enormous, from evaluating the identity of potential asylum seekers to pinpointing the Unabomber or perpetrators of crimes where language data is involved, but critics maintain that computer scans alone to evaluate language are insufficient and that overreliance can pose dangers.

"A small band of linguists, engineers and computer scientists, among others, are busy training computers to recognize hallmarks of what they call emotional speech - talk that reflects deception, anger, friendliness and even flirtation. ... Algorithms developed by Dr. Hirschberg and colleagues have been able to spot a liar 70 percent of the time in test situations, while people confronted with the same evidence had only 57 percent accuracy ... His lab has also found ways to use vocal cues to spot inebriation, though it hasn't yet had luck in making its computers detect humor - a hard task for the machines, he said."

Novelist Kurt Vonnegut once opined, "There is no reason why the simple shapes of stories can't be fed into computers. They are beautiful shapes." Thanks to new text-mining techniques, this has now been done. Researchers at the University of Vermont's Computational Story Lab have analysed over 1,700 English novels to reveal six basic story types - you could call them archetypes - that form the building blocks for more complex stories. They are: 1. Rags to riches - a steady rise from bad to good fortune 2. Riches to rags - a fall from good to bad, a tragedy 3. Icarus - a rise then a fall in fortune 4. Oedipus - a fall, a rise then a fall again 5. Cinderella - rise, fall, rise 6. Man in a hole - fall, rise The researchers used sentiment analysis to get the data - a statistical technique often used by marketeers to analyse social media posts in which each word is allocated a particular 'sentiment score', based on crowdsourced data. Depending on the lexicon chosen, a word can be categorised as positive (happy) or negative (sad), or it can be associated with one or more of eight more subtle emotions, including fear, joy, surprise and anticipation. For example, the word 'happy' is positive, and associated with joy, trust and anticipation. The word 'abolish' is negative and associated with anger. Do sentiment analysis on all the words in a novel, poem or play and plot the results against time, and it's possible to see how the mood changes over the course of the text, revealing a kind of emotional narrative. While not a perfect tool - it looks at words in isolation, ignoring context - it can be surprisingly insightful when applied to larger chunks of text

"Now in the 21st century, with sophisticated text-crunching tools at our disposal, it is possible to put Bridgman's theory to the test. Has a vernacular style become the standard for the typical fiction writer? Or is literary language still a distinct and peculiar beast?"

Imagine putting on a virtual reality headset and entering a world where you can explore communities, like Missoula, except your character, and everyone you interact with, speaks Salish, Cheyenne or Blackfoot. Imagine having a device like Amazon's Alexa that understands and speaks exclusively in Indigenous languages. Or imagine a digital language playground in Facebook's Metaverse, where programmers create interactive games to enhance Indigenous language learning. Michael Running Wolf, a Northern Cheyenne man who is earning his Ph.D. in computer science, wants to make these dreams a reality. Running Wolf grew up in Birney, a town with a population of 150 just south of the Northern Cheyenne Reservation. He spent most of his childhood living without electricity. Running Wolf can speak some Cheyenne, but he wants Indigenous language learning to be more accessible, immersive and engaging. And he believes artificial intelligence is the solution. Running Wolf is one of a handful of researchers worldwide who are studying Indigenous languages and AI. He works with a small team of linguists and data scientists, and together, they analyze Indigenous languages and work to translate them into something a computer can interpret. If his team can accomplish this, Running Wolf reasons, then perhaps AI can be used to help revitalize Indigenous languages everywhere.

"More than many nations, China is a place where names are imbued with deep significance. Western companies looking to bring their products to China face a problem not unlike that of Chinese parents naming a baby boy... And so the art of picking a brand name that resonates with Chinese consumers is no longer an art. It has become a sort of science, with consultants, computer programs and linguistic analyses to ensure that what tickles a Mandarin ear does not grate on a Cantonese one. ... Precisely why some Chinese words are so freighted with emotion is anyone's guess. But Denise Sabet, the vice general manager at Labbrand, suggests the reasons include cultural differences and the Chinese reliance on characters for words, rather than a phonetic alphabet. "

Teaching a baby to speak is more art than science. It begins with babble and almost like magic the child says mama and dada, then no, uh-oh, mine, especially mine. But sometimes children struggle to learn to speak. A team of linguists, computer scientists and psychologists in Britain think robots might help explain why that happens. They've created the world's first baby robot, DeeChee; white plastic skin and a smile of red lights and articulated hands that grab and gesture almost like an infant. Now, scientists hope that DeeChee's silicon brain will help explain what's going on in the minds of human babies, specifically, how sensitivity to particular sounds helps infants learn words.

A team of researchers at the Cincinnati Children's Hospital use computers to analyze the language of suicide notes, in the hope that they can better identify those at risk. By comparing patient interview responses to suicide notes, they can identify how similar or divergent their language is from the language of suicide.Here are three patterns researchers have identified in their corpus of authentic suicide notes: 1. Loss of hope. When hope is gone, when hopelessness emerges - and that's in most of the notes 2. Practical instruction, e.g.. "Remember to change the tires. Remember to change the oil. I drew a check, but I didn't put the money in. Please go ahead and make the deposit." 3. The presence of the following emotions: depression, a little bit of anger, abandonment, and the sense of "I just can't go on any longer. I can't deal with this any longer."

Languages often adapt naturally to the world around them. Speakers create new words to communicate new concepts. But when a language isn't spoken widely enough to adapt on its own - as with Hawaiian - it may need help to move things along. The Hawaiian language has nearly 30,000 words. But up until the late 1980s, the language didn't have words for subjects like soccer, computer or recycling. So a group of linguists and language advocates formed a lexicon committee in 1987 to invent new words. The committee has created at least 7,500 new words since its inception. Many of the committee's entries have been published in a modern Hawaiian language dictionary called Māmāka Kaiao. Much of the group's work helped to make Hawaiian teachable in language immersion schools. But some are skeptical of the committee's work. One interviewee noted that there is a small group creating words that we "need" now, but it's unclear why that word was chosen or how. Even the pronunciation of new words can be confusing, she adds. Disagreements among Hawaiian speakers may seem like bad news for spreading the language. But Larry Kimura, UH-Hilo Hawaiian language professor, says it's a sign that the language is growing. He said the lexicon committee helps speed up what would have been an otherwise natural process of ʻōlelo Hawaiʻi adapting to the world around it.

This article explains the new system created for air traffic controllers and pilots. AcListant is a system that will listen to air controllers' radio conversations to help make suggestions for commands that fit the situation. The system can filter through basic greetings like "Hello" and "Good Morning" and focus on commands instead. AcListant can help with better communication especially with pilots who speak very fast or with an accent.

A traveling journalist in Beijing utilizes both Baidu (China's version of Google) and Google voice-translation apps with mixed results. You speak into the apps, they listen and then translate into the language you choose. They do it in writing, by displaying text on the screen as you talk; and out loud, by using your phone's speaker to narrate what you've said once you're done talking. Typically exchanges are brief: 3-4 turns on average for Google, 7-8 for Baidu's translate app. Both Google and Baidu use machine learning to power their translation technology. While a human linguist could dictate all the rules for going from one language to another, that would be tedious, and yield poor results because a lot of languages aren't structured in parallel form. So instead, both companies have moved to pattern recognition through "neural machine translation." They take a mountain of data - really good translations - and load it into their computers. Algorithms then mine through the data to look for patterns. The end product is translation that's not just phrase-by-phrase, but entire thoughts and sentences at a time. Not surprisingly, sometimes translations are successes, and other times, epic fails. Why? As Macduff Hughes, a Google executive, notes, "there's a lot more to translation than mapping one word to another. The cultural understanding is something that's hard to fully capture just in translation."