I need to identify difficult words from input text. I do not want to use common word lists because the level of difficulty will need to be set for children. Is there a scoring mechanism that computes the difficulty level of each word? I can use a threshold for score to separate difficult words from easy ones. The end objective is to provide word meanings for all such difficult words.
There are several ways in which overall text can be scored for complexity or difficulty level, eg. The Dale–Chall formula, The Gunning fog formula, etc. However, these are used for defining "readability" i.e. the ease with which a reader can understand the written text. My requirement is related to the difficulty level of individual words in a text.
There are a few methods that I have come across for defining difficult words such words with more than 2 syllables or any word not appearing in the 10000 most common words, etc. However, none of these methods are useful for me. I am trying to build an application that can identify difficult words, and provides relevant dictionary meanings for only those words. Is there a scoring mechanism that will allow me to use a threshold to separate the difficult words from the easy ones?
Related
I have text data from two different groups. In total I have around 4000 text passages with around 300 words.
I am searching for a tool that allows me to analyze the difference between these two groups.
In the best case, this tool can analyze different dimensions, e.g. the length of sentences, usage of superlatives, perspective of the narrator, usage of passive form, clear and objective writing VS hedging and imprecise writing.
In Python, you can use the nltk or spacey packages to process the texts so that you can analyze them (using pandas, for example). But there's not ready-made software (as far as I know) that will do all of that for you. You're going to have to write your own code.
For example, you would create a pandas dataframe with a row for all of the texts, with their group ('A' or 'B' or whatever) as one of the columns and the raw text as the other. Then you use nltk to tokenize the text and do whatever other preprocessing you want to do, storing the clean, tokenized text in another column. Then you can have a column for, for example, sentence length (which you can compute using nltk). From there you'll be able to get the means of the two groups, standard deviation, statistical significance of difference, etc.
It's straightforward for something like sentence length, but the other features you mention are more difficult. What does it mean for a text to be clear and objective, or hedged and imprecise? That means nothing on its own: you have to decide what exactly you mean by that, and what features characterize it. For example, you could make a list of hedgers ('I think', 'may', 'might', 'I'm not sure but', etc.) and then count their frequency in each text.
Something like "perspective of the narrator" might need to be annotated manually, depending on what you mean by it. If you just mean 1st person vs. 3rd person, that could be easy to identify (compare the 'I's vs. the 'he/she's), but anything more subtle than that, I'm not sure how you'd do it.
Good luck with your project!
I'm playing around with a recommendation system that takes key descriptive words and phrases and matches them against others. Specifically, I'm focusing on flavors in beer, with an algorithm searching for things like malty or medium bitterness, pulling those out, and then comparing against other beers to come up with flavor recommendations.
Currently, I'm struggling with the extraction. What are some techniques for identifying words and standardizing them for later processing?
How do I pull out hoppy and hops and treat them as the same word, but also keeping in mind that very hoppy and not enough hops have different meanings that are modified by the preceding word(s)? I believe I can use stemming for things like plurals and suffixed/prefixed words, but what about pairs or more complicated patterns? What techniques exist for this?
I would first ignore the finer-grained distinctions and compile a list of lexico-semantic patterns, which can be used to extract some information structure-- for example:
<foodstuff> has a <taste-description> taste
<foodstuff> tastes <taste-description>
very <taste-description>
not enough <taste-description>
You can use instances of such patterns in your text to infer useful concepts (such as different taste descriptions) which can then be used again in order to bootstrap the extraction of new patterns and thus new concepts.
Let’s say I have a list of 250 words, which may consist of unique entries throughout, or a bunch of words in all their grammatical forms, or all sorts of words in a particular grammatical form (e.g. all in the past tense). I also have a corpus of text that has conveniently been split up into a database of sections, perhaps 150 words each (maybe I would like to determine these sections dynamically in the future, but I shall leave it for now).
My question is this: What is a useful way to get those sections out of the corpus that contain most of my 250 words?
I have looked at a few full text search engines like Lucene, but am not sure they are built to handle long query lists. Bloom filters seem interesting as well. I feel most comfortable in Perl, but if there is something fancy in Ruby or Python, I am happy to learn. Performance is not an issue at this point.
The use case of such a program is in language teaching, where it would be nice to have a variety of word lists that mirror the different extents of learner knowledge, and to quickly find fitting bits of text or examples from original sources. Also, I am just curious to know how to do this.
Effectively what I am looking for is document comparison. I have found a way to rank texts by similarity to a given document, in PostgreSQL.
I have an algorithm (that I can't change) that outputs a list of phrases. These phrases are intended to be "topics". However, some of them are meaningless on their own. Take this list:
is the fear
freesat
are more likely to
first sight
an hour of
sue apple
depression and
itunes
How can I filter out those phrases that don't make sense on their own, to leave a list like the following?
freesat
first sight
sue apple
itunes
This will be applied to sets of phrases in many languages, but English is the priority.
It's got to be grammatically acceptable in that it can't rely on other words in the original sentence that it was extracted from; e.g. it can't end in 'and'.
Although this is still an underspecified question, it sounds like you want some kind of grammar checker. I suggest you try applying a part-of-speech tagger to each phrase, compile a list of patterns of POS tags that are acceptable (e.g. anything that ends in a preposition would be unacceptable) and use that to filter your input.
At a high level, it seems that phrases which were only nouns or adjective-noun combos would give much better results.
Examples:
"Blue Shirt"
"Happy People"
"Book"
First of all, this problem can be as complex as you want it to be. For third-party reading/solutions, I came across:
http://en.wikipedia.org/wiki/List_of_natural_language_processing_toolkits
http://research.microsoft.com/en-us/groups/nlp/
http://sharpnlp.codeplex.com/ (note the part of speech tagger)
If you need 100% accuracy, then I wouldn't write such a tool myself.
However, if the problem domain is limited...
I would start by throwing out conjunctions, prepositions, contractions, state-of-being verbs, etc. This is a fairly short list in English (and looks very similar to the stopwords which #HappyTimeGopher suggested).
After that, you could create a dictionary (as an indexed structure, of course) of all acceptable nouns and adjectives and compare each word in the raw phrases to that. Anything which didn't occur in the dictionary and occur in the correct sequence could be thrown out or ranked lower.
This could be useful if you were given 100 input values and wanted to select the best 5. Finding the values in the dictionary would mean that it's likely the word/phrase was good.
I've auto-generated such a dictionary before by building a raw index from thousands of documents pertaining to a vertical industry. I then spent a few hours with SQL and Excel stripping out problems easily spotted by a human. The resulting list wasn't perfect but it eliminated most of the blatantly dumb/pointless terminology.
As you may have guessed, none of this is foolproof, although checking adjective-to-noun sequence would help somewhat. Consider the case of "Greatest Hits" versus "Car Hits [Wall]".
Proper nouns (e.g. person names) don't work well with the dictionary approach, since it's probably not feasible to build a dictionary of all variations of given/surnames.
To summarize:
use a list of stopwords
generate a dictionary of words, classifying them with a part of speech(s)
run raw phrases through dictionary and stopwords
(optional) rank on how confident you are on a match
if needed, accept phrases which didn't violate known patterns (this would handle many proper nouns)
If you've access to the text these phrases were generated from, it may be easier to just create your own topic tags.
Failing that, I'd probably just remove anything that contained a stop word. See this list, for example:
http://www.ranks.nl/resources/stopwords.html
I wouldn't break out POS tagging or anything stronger for this.
It seems you could create a list that filters out three things:
Prepositions: https://en.wikipedia.org/wiki/List_of_English_prepositions
Conjunctions: https://en.wikipedia.org/wiki/Conjunction_(grammar)
Verb forms of to-be: http://www.englishplus.com/grammar/00000040.htm
If you filter on these things you'd get pretty far. Are you more concerned with false negatives or positives? If false negatives aren't a huge problem, this is how I would approach it.
How would you go about parsing a string of free form text to detect things like locations and names based on a dictionary of location and names? In my particular application there will be tens of thousands if not more entries in my dictionaries so I'm pretty sure just running through them all is out of the question. Also, is there any way to add "fuzzy" matching so that you can also detect substrings that are within x edits of a dictionary word? If I'm not mistaken this falls within the field of natural language processing and more specifically named entity recognition (NER); however, my attempt to find information about the algorithms and processes behind NER have come up empty. I'd prefer to use Python for this as I'm most familiar with that although I'm open to looking at other solutions.
You might try downloading the Stanford Named Entity Recognizer:
http://nlp.stanford.edu/software/CRF-NER.shtml
If you don't want to use someone else's code and you want to do it yourself, I'd suggest taking a look at the algorithm in their associated paper, because the Conditional Random Field model that they use for this has become a fairly common approach to NER.
I'm not sure exactly how to answer the second part of your question on looking for substrings without more details. You could modify the Stanford program, or you could use a part-of-speech tagger to mark proper nouns in the text. That wouldn't distinguish locations from names, but it would make it very simple to find words that are x words away from each proper noun.