Introduction to Natural Language Processing, Part 1: Lexical Units
This series explores core concepts of natural language processing, starting with an introduction to the field and explaining how to identify lexical units as a part of data preprocessing.
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In this series, we will explore core concepts related to the study and application of natural language processing. Part one below provides an introduction to the field and explains how to identify lexical units as a means of data preprocessing.
Introduction to Natural Language Processing
Natural language processing is a set of techniques that allows computers and people to interact. Consider the process of extracting information from some data generating process: A company wants to predict user traffic on its website so it can provide enough compute resources (server hardware) to service demand. Engineers can define the relevant information to be the amount of data requested. Because they control the data generating process, they can add logic to the website that stores every request for data as a variable. Then, they can define the unit of measurement as the amount of data requested as a byte, in turn allowing us to represent the information as integers. With an excellent representation of the information in hand, the engineers can store it in a tabular database so analysts can make predictions based on this historical data.
Natural language processing is the application of the steps above — defining representations of information, parsing that information from the data generating process, and constructing, storing, and using data structures that store information — to information embedded in natural languages.
What makes a language natural is precisely what makes natural language processing difficult; the rules governing the representation of information in natural languages evolved without predetermination. These rules can be high level and abstract, such as how sarcasm is used to convey meaning; or quite low level, such as using the character "s" to denote plurality of nouns. Natural language processing involves identifying and exploiting these rules with code to translate unstructured language data into information with a schema. Language data may be formal and textual, such as newspaper articles, or informal and auditory, such as a recording of a telephone conversation. Language expressions from different contexts and data sources will have varying rules of grammar, syntax, and semantics. Strategies for extracting and representing information from natural languages that work in one setting often fail in others.
Companies often have access to records of natural language that contain valuable information. Product reviews or even tweets on Twitter can contain specific complaints or feature requests related to a product that can help prioritize and evaluate proposals. Online marketplaces may have item descriptions available that can help define a taxonomy of products. A digital newspaper may have an archive of online articles that can be used to build a search engine to allow users to find relevant content. Information that is representational of natural language can also be useful for building powerful applications, such as bots that respond to questions or software that translates from one language to another.
Natural language processing can be used to identify specific complaints from text.
Projects requiring natural language processing are generally organized by these sorts of challenges. Solving them usually requires us to serially piece multiple subtasks together, where there may be many approaches for each subtask. The universe of natural language processing methods can be daunting, as it's highly specialized, vast, and somewhat lacking in an overarching conceptual framework. While a complete summary of natural language processing is well beyond the scope of this article, we will cover some concepts that are commonly used in general purpose natural language processing work. We'll assume that we have access to textual data with which to work (not auditory, which requires the additional step of speech recognition).
Identifying Lexical Units
As natural languages are generally composed of words, an initial step of many natural language processing projects is identifying words within some raw text. The concept of a word, however, may be too restrictive or ambiguous. The strings "cats" and "cat" are different forms of the same entry in the dictionary; should they be treated equivalently? "Star Wars" has no entry in the dictionary, and though it contains a space, we think of it a singular entity. These are the sorts of challenges involved in defining lexical units, which represent basic elements of a vocabulary.
For a given task, the researcher must define what constitutes an appropriate lexical unit. Should singular and plural forms of a word be considered to belong to the same lexical unit? Assume we are building a question answering system, and receive the following queries:
A: "Find the closest theaters to me."
B: "Find the closest theater to me."
In A, the user is implying that she wants to view multiple theaters, whereas in B, she just wants the single closest theater. Throwing away the distinction between singular and plural will degrade the quality of our application.
Alternatively, assume that we want to summarize the following product reviews:
A: "The product had some connectivity problems."
B: "The product had a connectivity problem."
Here, the distinction between "problems" and "problem" is not relevant.
While not at all exhaustive, tokenization and normalization are two common steps to parsing lexical units from natural language. Tokenization is the process of separating a sequence of characters into tokens, each of which represents an instance of a term. Normalization is the set of steps we take to condense terms into lexical units.
Tokenization algorithms can be simple and deterministic, such as separating characters into tokens every time a character is not alphanumeric. Non-alphanumeric characters could be spaces, punctuation marks, hashtags, etc. We can implement this approach with pattern matching, checking for the presence of characters or character sequences according to some rules where we define these patterns as regular expressions (often abbreviated "regex"). For example, we can denote the set of all numeric characters with regex string "0-9" or "\d." We can combine terms with modifiers into complex search patterns. We can split characters on non-alphanumeric characters by splitting every time the query
[^A-Za-z0-9]+ is matched. Application of regular expressions is so widespread that implementations are available in almost all modern programming languages.
It's easy to find examples where this strategy will fail ("didn't ≠ ["didn", "t"]). In some applications, researchers capture these patterns with multiple complex regex queries and morphology-specific rules, and pass the text input through a finite state machine to determine the correct tokenization. Encoding an exhaustive set of rules can be difficult, and will depend on the application and the type of text data under analysis (though there are some partial rulesets that have been assembled). To adapt to a new corpus, tokenizers can be built by training statistical models on hand-tokenized text, though this approach is rarely used in practice due to the success of deterministic approaches. These models may look at sequences of character properties, such as whether the contiguous alphanumeric begins with a capital letter, and model tokenizations as a function of these properties. The rules the model learns to use will depend on the provided training corpus (Twitter, medical articles, etc.).
Another challenge is that multi-term character sequences can comprise a lexical unit. These multi-term sequences could be named entities such as "New York," in which we case, we are doing named entity recognition or simply common phrases. One approach to this problem is to allow for some redundancy in our representation by including all n-length sets of terms (n-grams) as tokens. If we limit ourselves to unigrams and bigrams, New York would be tokenized as "New,""York,""New York." To add some sophistication instead of exhausting all n-grams, we could select the highest order n-gram representation of a set of terms subject to some condition, like whether it exists in a hard-coded dictionary (called a gazetteer) or if it is common in our dataset. Alternatively, we could employ machine learning models. If the probability of a given word, p(wordi | wordi-1), is high enough given the preceding word, we might consider the sequence a two-term lexical unit (alternatively, we could use the formulation p(wordi | wordi-1 = bigram) using labeled examples). Though smoothing can help ameliorate the problem, these language models tend to have trouble generalizing, and require some amount of transfer learning, feature engineering, determinism, or abstraction. Probabilistic n-gram models require labeled examples, machine learning algorithms, and feature extractors (the latter two are bundled in Stanford's NER software). If such models are not worth the investment, high quality pretrained models can often be used successfully on new data sets.
After tokenization, we may wish to normalize our tokens. Normalization is a set of rules that aim to reduce all instances of a lexical equivalence class to their canonical form.
This may include procedures like:
- plural -> singular (e.g. cats -> cat)
- past tense -> present tense (e.g. ran -> run)
- adverb form -> adjective form (quickly -> quick)
- hyphenation -> concatenation
One approach to normalization is to reduce a word to its dictionary form through a process called lemmatisation. The lemma of a term has all the metadata contained in a dictionary entry: part of speech, definition (word sense), and stem. Lemmatisation traditionally requires a morphological parser, in which we completely featurize some unprocessed term (tense, plurality, part of speech, etc.) based on its morphological elements (prefix, suffix, etc.). To build a parser, we create a dictionary of known stems and affixes (lexicon) with metadata about them, like possible parts of speech, enumerate the rules (morphotactics) governing how morphemes can be compiled together (plural modifier "-s" must follow the noun, for example), and finally enumerate rules (orthographic rules) that govern changes in a word under different morphological states (for instance, a past tense verb ending in "-c" must have a "k" added, such as "picnic -> picnicked"). These rules and terms are passed to a finite state machine that pass over some input, maintaining a state or set of feature values that is then updated as rules and lexicon are checked against the text (similar to how regular expressions work).
The degree of specificity in how we define the equivalence class (which morphological elements are relevant) and, thus, the degree of normalization we use and morphological metadata we extract depends on our application. In information retrieval, we often only care about the high-level semantics of some text. All morphemes, or meaning-conveying elements of the word other than the stem, can be discarded along with all morphological metadata (such as tense). Stemming algorithms usually use substitution rules, such as:
- if a given stem contains a vowel, we remove "-ing" from all tokens that contain the stem ("barking" → "bark" but "string" → "string")
The Porter stemmer is probably the most prominent substitution-type stemming algorithm, though there are others. All of these types of stemmers are error prone, but are simple and easy to implement and are good starting points for most applied work.
Ultimately, the goals of building a preprocessing pipeline include:
- All relevant information is extracted
- All irrelevant information is discarded
- Text is represented in sufficiently few equivalence classes
- The data is in a useful form, such as lists of ascii-encoded strings or as abstract data structures such as sentences or documents
The definition of relevant, sufficient, and useful depends on the requirements of the project, the strength of the development team, and availability of time and resources. Furthermore, in many cases, the strength of a preprocessing pipeline can determine the overall success of a project. Therefore, care should be taken while building a pipeline.
After translating raw text into a string or tokenized array of lexical units, the researcher or developer may take steps to preprocess his or her text data, such as string encoding, stop word and punctuation removal, spelling correction, part-of-speech tagging, chunking, sentence segmentation, and syntax parsing. The table below illustrates some examples of the types of processing a researcher may use given a task and some raw text:
|Raw Text||Processed||Steps||Task||How Pipeline Suits Task|
|She sells seashells by the seashore.||['she','sell','seashell','seashore']||tokenization, lemmatization, stop word removal, punctuation removal||topic modeling||We only care about high level, thematic, and semantically heavy words|
|John is capable.||John/PROPN is/VERB capable/ADJ ./PUNCT||tokenization, part of speech tagging||named entity recognition||We care about every word, but want to indicate the role each word plays to build a list of NER candidates|
|Who won? I didn't check the scores.||[u'who', u'win'], [u'i',u'do',u'not',u'check',u'score']||tokenization, lemmatization, sentence segmentation, punctuation removal, string encoding||sentiment analysis||We need all words, including negations since they can negate positive statements, but don't care about tense or word form|
After preprocessing, we often need to take additional steps to represent the information in some text quantitatively. In part two of this series, we'll discuss various abstracted and numerical representations of text.
The dependency plots included in this post were build using the awesome open source library DisplaCy. Check it out here.
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